{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HW1 布尔查询之BSBI与索引压缩\n",
    "\n",
    "本次作业使用斯坦福大学[CS 276 / LING 286: Information Retrieval and Web Search](https://web.stanford.edu/class/cs276/)课程的代码框架来实现。具体来说主要包含的内容有：\n",
    "1. [索引构建 (40%)](#索引构建与检索-(40%)) 使用BSBI方法模拟在内存不足的情况下的索引构建方式，并应用于布尔查询\n",
    "2. [索引压缩 (30%)](#索引压缩-(30%)) 使用可变长编码对构建的索引进行压缩\n",
    "3. [布尔检索 (10%)](#布尔联合检索-(10%)) 对空格分隔的单词查询进行联合（与）布尔检索\n",
    "3. [实验报告 (10%)](#Report-(25%)) 描述你的代码并回答一些问题\n",
    "4. [额外的编码方式 (10%)](#额外的编码方式-(10%)) 鼓励使用额外的编码方式对索引进行压缩 (例如, gamma-encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can add additional imports here\n",
    "import sys\n",
    "import pickle as pkl\n",
    "import array\n",
    "import os\n",
    "import timeit\n",
    "import contextlib\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实验使用的文本数据是stanford.edu域下的网页内容，可从http://web.stanford.edu/class/cs276/pa/pa1-data.zip 下载。以下代码将大约170MB的文本数据下载到当前目录下，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "data_url = 'http://web.stanford.edu/class/cs276/pa/pa1-data.zip'\n",
    "data_dir = 'pa1-data'\n",
    "urllib.request.urlretrieve(data_url, data_dir+'.zip')\n",
    "zip_ref = zipfile.ZipFile(data_dir+'.zip', 'r')\n",
    "zip_ref.extractall()\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之后构建的索引会被存储到`output_dir`，`tmp`会存储测试数据（toy-data）所生成的一些临时文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: \n",
    "    os.mkdir('output_dir')\n",
    "except FileExistsError:\n",
    "    pass\n",
    "try: \n",
    "    os.mkdir('tmp')\n",
    "except FileExistsError:\n",
    "    pass\n",
    "try: \n",
    "    os.mkdir('toy_output_dir')\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在数据目录下有10个子目录（命名0-9）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(os.listdir('pa1-data'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每一个子目录下的文件都包含一个独立网页的内容。可以认为在同一子目录下没有同名文件，即每个文件的绝对路径不会相同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(os.listdir('pa1-data/0'))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有的网页内容已经经过处理，仅包含由空格分隔开的单词，不再需要进行额外的标准化工作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('pa1-data/0/3dradiology.stanford.edu_', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 索引构建与检索 (40%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作业的第一部分是使用**blocked sort-based indexing (BSBI)** 算法来构建倒排索引并实现布尔检索。关于BSBI算法可以参考老师课件或者斯坦福教材[Section 4.2](http://nlp.stanford.edu/IR-book/pdf/04const.pdf)。以下摘自教材内容\n",
    "\n",
    "> To construct an index, we first make a pass through the collection assembling all term-docID pairs. We then sort the pairs with the term as the dominant key and docID as the secondary key. Finally, we organize the docIDs for each term into a postings list and compute statistics like term and document frequency. For small collections, all this can be done in memory. \n",
    "\n",
    "对于无法在内存一次性处理的较大数据集，将会使用到二级存储（如：磁盘）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IdMap (6%)\n",
    "\n",
    "再次引用教材 Section 4.2:\n",
    "\n",
    "> To make index construction more efficient, we represent terms as termIDs (instead of strings), where each termID is a unique serial number. We can build the mapping from terms to termIDs on the fly while we are processing the collection. Similarly, we also represent documents as docIDs (instead of strings).\n",
    "\n",
    "我们首先定义一个辅助类`IdMap`，用于将字符串和数字ID进行相互映射，以满足我们在term和termID、doc和docID间转换的需求。\n",
    "\n",
    "实现以下代码中的`_get_str` 和 `_get_id`函数，IdMap类的唯一接口是`__getitem__`，它是一个特殊函数，重写了下标运算`[]`,根据下标运算键的类型得到正确的映射值（如果不存在需要添加）。（特殊函数可参考[官方文档](https://docs.python.org/3.7/reference/datamodel.html#special-method-names)）\n",
    "<br>\n",
    "<br>\n",
    "我们会用到字典来将字符串转换为数字，用列表来将数字转换为字符串。(4%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class IdMap:\n",
    "    \"\"\"Helper class to store a mapping from strings to ids.\"\"\"\n",
    "    def __init__(self):\n",
    "        self.str_to_id = {}\n",
    "        self.id_to_str = []\n",
    "        \n",
    "    def __len__(self):\n",
    "        \"\"\"Return number of terms stored in the IdMap\"\"\"\n",
    "        return len(self.id_to_str)\n",
    "        \n",
    "    def _get_str(self, i):\n",
    "        \"\"\"Returns the string corresponding to a given id (`i`).\"\"\"\n",
    "        ### Begin your code\n",
    "        if 0 <= i < len(self.id_to_str):\n",
    "            return self.id_to_str[i]\n",
    "        else:\n",
    "            return None\n",
    "        ### End your code\n",
    "        \n",
    "    def _get_id(self, s):\n",
    "        \"\"\"Returns the id corresponding to a string (`s`). \n",
    "        If `s` is not in the IdMap yet, then assigns a new id and returns the new id.\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        if s not in self.str_to_id:\n",
    "            i = len(self.id_to_str)\n",
    "            self.str_to_id[s] = i\n",
    "            self.id_to_str.append(s)\n",
    "            return i\n",
    "        else:\n",
    "            return self.str_to_id[s]\n",
    "        ### End your code\n",
    "            \n",
    "    def __getitem__(self, key):\n",
    "        \"\"\"If `key` is a integer, use _get_str; \n",
    "           If `key` is a string, use _get_id;\"\"\"\n",
    "        if type(key) is int:\n",
    "            return self._get_str(key)\n",
    "        elif type(key) is str:\n",
    "            return self._get_id(key)\n",
    "        else:\n",
    "            raise TypeError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "确保代码能通过以下简单测试样例 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "testIdMap = IdMap()\n",
    "assert testIdMap['a'] == 0, \"Unable to add a new string to the IdMap\"\n",
    "assert testIdMap['bcd'] == 1, \"Unable to add a new string to the IdMap\"\n",
    "assert testIdMap['a'] == 0, \"Unable to retrieve the id of an existing string\"\n",
    "assert testIdMap[1] == 'bcd', \"Unable to retrive the string corresponding to a\\\n",
    "                                given id\"\n",
    "try:\n",
    "    testIdMap[2]\n",
    "except IndexError as e:\n",
    "    assert True, \"Doesn't throw an IndexError for out of range numeric ids\"\n",
    "assert len(testIdMap) == 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之后会需要你自己来写测试样例来确保你的程序正常运行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将倒排列表编码成字节数组 (2%)\n",
    "\n",
    "为了高效地从磁盘读写倒排列表（文档ID），我们将其存储为字节数组的形式。代码提供了`UncompressedPostings`类来用静态函数实现对倒排列表的编码和解码。在之后的任务中你需要使用该接口实现索引压缩版本（可变长编码）。\n",
    "\n",
    "参考:\n",
    "1. https://docs.python.org/3/library/array.html\n",
    "2. https://pymotw.com/3/array/#module-array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "class UncompressedPostings:\n",
    "    \n",
    "    @staticmethod\n",
    "    def encode(postings_list):\n",
    "        \"\"\"Encodes postings_list into a stream of bytes\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        postings_list: List[int]\n",
    "            List of docIDs (postings)\n",
    "            \n",
    "        Returns\n",
    "        -------\n",
    "        bytes\n",
    "            bytearray representing integers in the postings_list\n",
    "        \"\"\"\n",
    "        return array.array('L', postings_list).tobytes()\n",
    "        \n",
    "    @staticmethod\n",
    "    def decode(encoded_postings_list):\n",
    "        \"\"\"Decodes postings_list from a stream of bytes\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        encoded_postings_list: bytes\n",
    "            bytearray representing encoded postings list as output by encode \n",
    "            function\n",
    "            \n",
    "        Returns\n",
    "        -------\n",
    "        List[int]\n",
    "            Decoded list of docIDs from encoded_postings_list\n",
    "        \"\"\"\n",
    "        \n",
    "        decoded_postings_list = array.array('L')\n",
    "        decoded_postings_list.frombytes(encoded_postings_list)\n",
    "        return decoded_postings_list.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行以下代码查看其工作方式 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "x = UncompressedPostings.encode([1,2,3])\n",
    "print(x)\n",
    "print(UncompressedPostings.decode(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 磁盘上的倒排索引 (2%)\n",
    "\n",
    "> With main memory insufficient, we need to use an external sorting algorithm, that is, one that uses disk. For acceptable speed, the central requirement of such an algorithm is that it minimize the number of random disk seeks during sorting - sequential disk reads are far faster than seeks. \n",
    "\n",
    "在这一部分我们提供了一个基类`InvertedIndex`，之后会在此基础上构建它的子类`InvertedIndexWriter`, `InvertedIndexIterator` 和 `InvertedIndexMapper`。在Python中我们常用`cPickle`进行序列化，但是它并不支持部分读和部分写，无法满足BSBI算法的需要，所以我们需要定义自己的存储方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvertedIndex:\n",
    "    \"\"\"A class that implements efficient reads and writes of an inverted index \n",
    "    to disk\n",
    "    \n",
    "    Attributes\n",
    "    ----------\n",
    "    postings_dict: Dictionary mapping: termID->(start_position_in_index_file, \n",
    "                                                number_of_postings_in_list,\n",
    "                                               length_in_bytes_of_postings_list)\n",
    "        This is a dictionary that maps from termIDs to a 3-tuple of metadata \n",
    "        that is helpful in reading and writing the postings in the index file \n",
    "        to/from disk. This mapping is supposed to be kept in memory. \n",
    "        start_position_in_index_file is the position (in bytes) of the postings \n",
    "        list in the index file\n",
    "        number_of_postings_in_list is the number of postings (docIDs) in the \n",
    "        postings list\n",
    "        length_in_bytes_of_postings_list is the length of the byte \n",
    "        encoding of the postings list\n",
    "    \n",
    "    terms: List[int]\n",
    "        A list of termIDs to remember the order in which terms and their \n",
    "        postings lists were added to index. \n",
    "        \n",
    "        After Python 3.7 we technically no longer need it because a Python dict \n",
    "        is an OrderedDict, but since it is a relatively new feature, we still\n",
    "        maintain backward compatibility with a list to keep track of order of \n",
    "        insertion. \n",
    "    \"\"\"\n",
    "    def __init__(self, index_name, postings_encoding=None, directory=''):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        index_name (str): Name used to store files related to the index\n",
    "        postings_encoding: A class implementing static methods for encoding and \n",
    "            decoding lists of integers. Default is None, which gets replaced\n",
    "            with UncompressedPostings\n",
    "        directory (str): Directory where the index files will be stored\n",
    "        \"\"\"\n",
    "\n",
    "        self.index_file_path = os.path.join(directory, index_name+'.index')\n",
    "        self.metadata_file_path = os.path.join(directory, index_name+'.dict')\n",
    "\n",
    "        if postings_encoding is None:\n",
    "            self.postings_encoding = UncompressedPostings\n",
    "        else:\n",
    "            self.postings_encoding = postings_encoding\n",
    "        self.directory = directory\n",
    "\n",
    "        self.postings_dict = {}\n",
    "        self.terms = []         #Need to keep track of the order in which the \n",
    "                                #terms were inserted. Would be unnecessary \n",
    "                                #from Python 3.7 onwards\n",
    "\n",
    "    def __enter__(self):\n",
    "        \"\"\"Opens the index_file and loads metadata upon entering the context\"\"\"\n",
    "        # Open the index file\n",
    "        self.index_file = open(self.index_file_path, 'rb+')\n",
    "\n",
    "        # Load the postings dict and terms from the metadata file\n",
    "        with open(self.metadata_file_path, 'rb') as f:\n",
    "            self.postings_dict, self.terms = pkl.load(f)\n",
    "            self.term_iter = self.terms.__iter__()                       \n",
    "\n",
    "        return self\n",
    "    \n",
    "    def __exit__(self, exception_type, exception_value, traceback):\n",
    "        \"\"\"Closes the index_file and saves metadata upon exiting the context\"\"\"\n",
    "        # Close the index file\n",
    "        self.index_file.close()\n",
    "        \n",
    "        # Write the postings dict and terms to the metadata file\n",
    "        with open(self.metadata_file_path, 'wb') as f:\n",
    "            pkl.dump([self.postings_dict, self.terms], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为是在与磁盘上的文件进行交互，所以我们提供了`__enter__`和`__exit__`函数，它使得我们能够像使用python中文件IO一样使用`with`语句。（参考[上下文管理器官方文档](https://docs.python.org/3/library/contextlib.html)）\n",
    "\n",
    "以下是使用 `InvertedIndexWriter` 上下文管理器的实例: (2%)\n",
    "\n",
    "```python\n",
    "with InvertedIndexWriter('test', directory='tmp/') as index:\n",
    "    # Some code here\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引 (30%)\n",
    "\n",
    "> BSBI (i) segments the collection into parts of equal size, (ii) sorts the termID-docID pairs of each part in memory, (iii) stores intermediate sorted results on disk, and (iv) merges all intermediate results into the final index. \n",
    "\n",
    "你需要将每一个子目录当做一个块（block），并且在构建索引的过程中每次只能加载一个块（模拟内存不足）。注意到我们是将操作系统意义上的块进行了抽象。你可以认为每个块足够小，能被装载进内存。\n",
    "\n",
    "在这一部分，我们将阶段性地构造类`BSBIIndex`。函数`index`给出了BSBI算法的框架，而你的工作则是在接下来的部分中实现函数`parse_block`, `invert_write` 和 `merge`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do not make any changes here, they will be overwritten while grading\n",
    "class BSBIIndex:\n",
    "    \"\"\" \n",
    "    Attributes\n",
    "    ----------\n",
    "    term_id_map(IdMap): For mapping terms to termIDs\n",
    "    doc_id_map(IdMap): For mapping relative paths of documents (eg \n",
    "        0/3dradiology.stanford.edu_) to docIDs\n",
    "    data_dir(str): Path to data\n",
    "    output_dir(str): Path to output index files\n",
    "    index_name(str): Name assigned to index\n",
    "    postings_encoding: Encoding used for storing the postings.\n",
    "        The default (None) implies UncompressedPostings\n",
    "    \"\"\"\n",
    "    def __init__(self, data_dir, output_dir, index_name = \"BSBI\", \n",
    "                 postings_encoding = None):\n",
    "        self.term_id_map = IdMap()\n",
    "        self.doc_id_map = IdMap()\n",
    "        self.data_dir = data_dir\n",
    "        self.output_dir = output_dir\n",
    "        self.index_name = index_name\n",
    "        self.postings_encoding = postings_encoding\n",
    "\n",
    "        # Stores names of intermediate indices\n",
    "        self.intermediate_indices = []\n",
    "        \n",
    "    def save(self):\n",
    "        \"\"\"Dumps doc_id_map and term_id_map into output directory\"\"\"\n",
    "        \n",
    "        with open(os.path.join(self.output_dir, 'terms.dict'), 'wb') as f:\n",
    "            pkl.dump(self.term_id_map, f)\n",
    "        with open(os.path.join(self.output_dir, 'docs.dict'), 'wb') as f:\n",
    "            pkl.dump(self.doc_id_map, f)\n",
    "    \n",
    "    def load(self):\n",
    "        \"\"\"Loads doc_id_map and term_id_map from output directory\"\"\"\n",
    "        \n",
    "        with open(os.path.join(self.output_dir, 'terms.dict'), 'rb') as f:\n",
    "            self.term_id_map = pkl.load(f)\n",
    "        with open(os.path.join(self.output_dir, 'docs.dict'), 'rb') as f:\n",
    "            self.doc_id_map = pkl.load(f)\n",
    "            \n",
    "    def index(self):\n",
    "        \"\"\"Base indexing code\n",
    "        \n",
    "        This function loops through the data directories, \n",
    "        calls parse_block to parse the documents\n",
    "        calls invert_write, which inverts each block and writes to a new index\n",
    "        then saves the id maps and calls merge on the intermediate indices\n",
    "        \"\"\"\n",
    "        for block_dir_relative in sorted(next(os.walk(self.data_dir))[1]):\n",
    "            td_pairs = self.parse_block(block_dir_relative)\n",
    "            index_id = 'index_'+block_dir_relative\n",
    "            self.intermediate_indices.append(index_id)\n",
    "            with InvertedIndexWriter(index_id, directory=self.output_dir, \n",
    "                                     postings_encoding=\n",
    "                                     self.postings_encoding) as index:\n",
    "                self.invert_write(td_pairs, index)\n",
    "                td_pairs = None\n",
    "        self.save()\n",
    "        with InvertedIndexWriter(self.index_name, directory=self.output_dir, \n",
    "                                 postings_encoding=\n",
    "                                 self.postings_encoding) as merged_index:\n",
    "            with contextlib.ExitStack() as stack:\n",
    "                indices = [stack.enter_context(\n",
    "                    InvertedIndexIterator(index_id, \n",
    "                                          directory=self.output_dir, \n",
    "                                          postings_encoding=\n",
    "                                          self.postings_encoding)) \n",
    "                 for index_id in self.intermediate_indices]\n",
    "                self.merge(indices, merged_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解析 (10%)\n",
    "\n",
    "> The function `parse_block`  parses documents into termID-docID pairs and accumulates the pairs in memory until a block of a fixed size is full. We choose the block size to fit comfortably into memory to permit a fast in-memory sort. \n",
    "\n",
    "你需要将每一个子目录当做一个块，`parse_block`接收子目录路径作为参数。同一子目录下所有文件名都是不同的。 (5%)\n",
    "\n",
    "_注意 - 我们使用 `BSBIIndex` 继承 `BSBIIndex`, 这只是对一个已存在类添加新内容的简单方法。在这里只是用来切分类的定义（jupyter notebook内教学使用，无特殊含义）。_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BSBIIndex(BSBIIndex):            \n",
    "    def parse_block(self, block_dir_relative):\n",
    "        \"\"\"Parses a tokenized text file into termID-docID pairs\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        block_dir_relative : str\n",
    "            Relative Path to the directory that contains the files for the block\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        List[Tuple[Int, Int]]\n",
    "            Returns all the td_pairs extracted from the block\n",
    "        \n",
    "        Should use self.term_id_map and self.doc_id_map to get termIDs and docIDs.\n",
    "        These persist across calls to parse_block\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        td_pairs = []\n",
    "        block_dir_absolute = os.path.join(self.data_dir,block_dir_relative)\n",
    "        for file_name in os.listdir(block_dir_absolute):\n",
    "            file_path = os.path.join(block_dir_absolute,file_name)\n",
    "            docID = self.doc_id_map[file_path]\n",
    "            with open(file_path,'r',encoding = 'utf-8') as f:\n",
    "                content = f.read()\n",
    "                terms = content.split()\n",
    "            for term in terms:\n",
    "                termID = self.term_id_map[term]\n",
    "                td_pairs.append((termID,docID))\n",
    "        return td_pairs\n",
    "        ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "遍历指定目录下的所有文本文件，用doc_id_map把文件路径转成docID，用term_id_map把词项转成termID，把索引对(termID, docID)存入列表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察函数在测试数据上是否正常运行 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i'm fine , thank you\n",
      "\n",
      "hi hi\n",
      "how are you ?\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('toy-data/0/fine.txt', 'r') as f:\n",
    "    print(f.read())\n",
    "with open('toy-data/0/hello.txt', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0),\n",
       " (1, 0),\n",
       " (2, 0),\n",
       " (3, 0),\n",
       " (4, 0),\n",
       " (5, 1),\n",
       " (5, 1),\n",
       " (6, 1),\n",
       " (7, 1),\n",
       " (4, 1),\n",
       " (8, 1)]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toy_dir = 'toy-data'\n",
    "BSBI_instance = BSBIIndex(data_dir=toy_dir, output_dir = 'tmp/', index_name = 'toy')\n",
    "BSBI_instance.parse_block('0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写一些测试样例来确保`parse_block`方法正常运行（如：相同单词出现时是相同ID） (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9, 2),\n",
       " (10, 2),\n",
       " (11, 2),\n",
       " (4, 2),\n",
       " (2, 2),\n",
       " (12, 2),\n",
       " (11, 2),\n",
       " (4, 2),\n",
       " (13, 2),\n",
       " (8, 2),\n",
       " (12, 3),\n",
       " (12, 3),\n",
       " (12, 3)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Begin your code\n",
    "BSBI_instance.parse_block('1')\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bye,bye,bye三个相同单词的id相同"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 倒排表 (10%)\n",
    "\n",
    "> The block is then inverted and written to disk. Inversion involves two steps. First, we sort the termID-docID pairs. Next, we collect all termID-docID pairs with the same termID into a postings list, where a posting is simply a docID. The result, an inverted index for the block we have just read, is then written to disk.\n",
    "\n",
    "在这一部分我们添加函数`invert_write`来实现由termID-docID对构建倒排表。 \n",
    "\n",
    "但是，我们首先需要实现类`InvertedIndexWriter`。和列表类似，该类提供了append函数，但是倒排表不会存储在内存中而是直接写入到磁盘里。 (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvertedIndexWriter(InvertedIndex):\n",
    "    \"\"\"\"\"\"\n",
    "    def __enter__(self):\n",
    "        self.index_file = open(self.index_file_path, 'wb+')              \n",
    "        return self\n",
    "\n",
    "    def append(self, term, postings_list):\n",
    "        \"\"\"Appends the term and postings_list to end of the index file.\n",
    "        \n",
    "        This function does three things, \n",
    "        1. Encodes the postings_list using self.postings_encoding\n",
    "        2. Stores metadata in the form of self.terms and self.postings_dict\n",
    "           Note that self.postings_dict maps termID to a 3 tuple of \n",
    "           (start_position_in_index_file, \n",
    "           number_of_postings_in_list, \n",
    "           length_in_bytes_of_postings_list)\n",
    "        3. Appends the bytestream to the index file on disk\n",
    "\n",
    "        Hint: You might find it helpful to read the Python I/O docs\n",
    "        (https://docs.python.org/3/tutorial/inputoutput.html) for\n",
    "        information about appending to the end of a file.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        term:\n",
    "            term or termID is the unique identifier for the term\n",
    "        postings_list: List[Int]\n",
    "            List of docIDs where the term appears\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        encoded_postings = self.postings_encoding.encode(postings_list)\n",
    "        start_position_in_index_file = self.index_file.tell()\n",
    "        number_of_postings_in_list = len(postings_list)\n",
    "        length_in_bytes_of_postings_list = len(encoded_postings)\n",
    "        self.postings_dict[term] = (start_position_in_index_file, number_of_postings_in_list, length_in_bytes_of_postings_list)\n",
    "        self.terms.append(term)\n",
    "        self.index_file.write(encoded_postings)\n",
    "        ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用指定的编码方式将postings_list压缩为字节流。记录当前词项的起始位置、文档数量和字节长度，并存入postings_dict,维护有序的词项列表terms,将编码后的字节流写入索引文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管还没有实现读取索引的类，我们还是可以用以下测试代码检测我们的实现。 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "with InvertedIndexWriter('test', directory='tmp/') as index:\n",
    "    index.append(1, [2, 3, 4])\n",
    "    index.append(2, [3, 4, 5])\n",
    "    index.index_file.seek(0)\n",
    "    assert index.terms == [1,2], \"terms sequence incorrect\"\n",
    "    assert index.postings_dict == {1: (0, 3, len(UncompressedPostings.encode([2,3,4]))), \n",
    "                                   2: (len(UncompressedPostings.encode([2,3,4])), 3, \n",
    "                                       len(UncompressedPostings.encode([3,4,5])))}, \"postings_dict incorrect\"\n",
    "    assert UncompressedPostings.decode(index.index_file.read()) == [2, 3, 4, 3, 4, 5], \"postings on disk incorrect\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们实现 `invert_write`，它将解析得到的td_pairs转换成倒排表，并使用`InvertedIndexWriter` 类将其写入磁盘。 (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BSBIIndex(BSBIIndex):\n",
    "    def invert_write(self, td_pairs, index):\n",
    "        \"\"\"Inverts td_pairs into postings_lists and writes them to the given index\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        td_pairs: List[Tuple[Int, Int]]\n",
    "            List of termID-docID pairs\n",
    "        index: InvertedIndexWriter\n",
    "            Inverted index on disk corresponding to the block       \n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        postings_dict = defaultdict(list)\n",
    "        for termID,docID in td_pairs:\n",
    "            postings_dict[termID].append(docID)\n",
    "        for termID in sorted(postings_dict.keys()):\n",
    "            postings_list = postings_dict[termID]\n",
    "            index.append(termID,postings_list)\n",
    "        ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用defaultdict收集每个termID对应的所有docID，生成键值为termID的字典。对termID进行排序，确保索引有序存储，调用index.append()将每个termID及其对应的docID列表写入磁盘"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以在测试数据上读取一个块并观察倒排索引中包含的内容。\n",
    "仿照`InvertedIndexWriter`部分写一些测试样例。 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Begin your code\n",
    "td_pairs = BSBI_instance.parse_block('0')\n",
    "with InvertedIndexWriter('test1', directory='tmp/') as index:   \n",
    "    BSBI_instance.invert_write(td_pairs,index)\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在toy-data中读取数据块0，将倒排索引写入磁盘,tmp文件夹中出现test1.dict,test1.index文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 合并 (10%)\n",
    "> The algorithm simultaneously merges the ten blocks into one large merged index. To do the merging, we open all block files simultaneously, and maintain small read buffers for the ten blocks we are reading and a write buffer for the final merged index we are writing. \n",
    "\n",
    "Python中的迭代模型非常自然地符合我们维护一个小的读缓存的要求。我们可以迭代地从磁盘上每次读取文件的一个倒排列表。我们通过构建`InvertedIndex`的子类`InvertedIndexIterator`来完成这个迭代任务。 (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvertedIndexIterator(InvertedIndex):\n",
    "    \"\"\"\"\"\"\n",
    "    def __enter__(self):\n",
    "        \"\"\"Adds an initialization_hook to the __enter__ function of super class\n",
    "        \"\"\"\n",
    "        super().__enter__()\n",
    "        self._initialization_hook()\n",
    "        return self\n",
    "\n",
    "    def _initialization_hook(self):\n",
    "        \"\"\"Use this function to initialize the iterator\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        self.current_term_index = 0\n",
    "        self.term_iter = iter(self.terms)\n",
    "        ### End your code\n",
    "\n",
    "    def __iter__(self): \n",
    "        return self\n",
    "    \n",
    "    def __next__(self):\n",
    "        \"\"\"Returns the next (term, postings_list) pair in the index.\n",
    "        \n",
    "        Note: This function should only read a small amount of data from the \n",
    "        index file. In particular, you should not try to maintain the full \n",
    "        index file in memory.\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        term = next(self.term_iter)\n",
    "        \n",
    "        start_position,_,length = self.postings_dict[term]\n",
    "        self.index_file.seek(start_position)\n",
    "        encoded = self.index_file.read(length)\n",
    "        postings_list = self.postings_encoding.decode(encoded)\n",
    "        return (term,postings_list)\n",
    "        ### End your code\n",
    "\n",
    "    def delete_from_disk(self):\n",
    "        \"\"\"Marks the index for deletion upon exit. Useful for temporary indices\n",
    "        \"\"\"\n",
    "        self.delete_upon_exit = True\n",
    "\n",
    "    def __exit__(self, exception_type, exception_value, traceback):\n",
    "        \"\"\"Delete the index file upon exiting the context along with the\n",
    "        functions of the super class __exit__ function\"\"\"\n",
    "        self.index_file.close()\n",
    "        if hasattr(self, 'delete_upon_exit') and self.delete_upon_exit:\n",
    "            os.remove(self.index_file_path)\n",
    "            os.remove(self.metadata_file_path)\n",
    "        else:\n",
    "            with open(self.metadata_file_path, 'wb') as f:\n",
    "                pkl.dump([self.postings_dict, self.terms], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化指针和迭代器。\n",
    "按顺序获取每个词项，使用start_position,length定位磁盘上的数据位置，解码后返回(词项，倒排表)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了测试以上代码，我们先用`InvertedIndexWriter` 创建索引，然后再迭代遍历。写一些小的测试样例观察它是否正常运行。至少你得写一个测试，手工构建一个小的索引，用`InvertedIndexWriter`将他们写入磁盘，然后用`InvertedIndexIterator`迭代遍历倒排索引。 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, [2, 3, 4]), (2, [3, 4, 5]), (3, [4, 5, 6])]\n"
     ]
    }
   ],
   "source": [
    "### Begin your code\n",
    "with InvertedIndexWriter('test2', directory='tmp/') as index:\n",
    "    index.append(1, [2, 3, 4])\n",
    "    index.append(2, [3, 4, 5])\n",
    "    index.append(3, [4, 5, 6])\n",
    "with InvertedIndexIterator('test2', directory='tmp/') as index:\n",
    "    print(list(index))\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> During merging, in each iteration, we select the lowest termID that has not been processed yet using a priority queue or a similar data structure. All postings lists for this termID are read and merged, and the merged list is written back to disk. Each read buffer is refilled from its file when necessary.\n",
    "\n",
    "我们将使用`InvertedIndexIterator`来完成读取的部分，用`InvertedIndexWriter`来将合并后的索引写入磁盘。\n",
    "\n",
    "注意到我们之前一直使用`with` 语句来打开倒排索引文件，但是现在需要程序化地完成这项工作。可以看到我们在基类`BSBIIndex`的`index`函数中使用了`contextlib`([参考文档](https://docs.python.org/3/library/contextlib.html#contextlib.ExitStack))\n",
    "你的任务是合并*打开的*`InvertedIndexIterator`对象（倒排列表），并且通过一个`InvertedIndexWriter`对象每次写入一个文档列表。\n",
    "\n",
    "既然我们知道文档列表已经排过序了，那么我们可以在线性时间内对它们进行合并排序。事实上`heapq`([参考文档](https://docs.python.org/3.0/library/heapq.html)) 是Python中一个实现了堆排序算法的标准模板。另外它还包含一个实用的函数`heapq.merge`，可以将多个已排好序的输入（可迭代）合并成一个有序的输出并返回它的迭代器。它不仅可以用来合并倒排列表，也可以合并倒排索引词典。\n",
    "\n",
    "为了让你快速上手`heapq.merge`函数，我们提供了一个简单的使用样例。给出两个以动物和树平均寿命排序的列表，我们希望合并它们。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Giraffe', 28)\n",
      "('Rhinoceros', 40)\n",
      "('Gray Birch', 50)\n",
      "('Indian Elephant', 70)\n",
      "('Black Willow', 70)\n",
      "('Golden Eagle', 80)\n",
      "('Basswood', 100)\n",
      "('Box turtle', 123)\n",
      "('Bald Cypress', 600)\n"
     ]
    }
   ],
   "source": [
    "import heapq\n",
    "animal_lifespans = [('Giraffe', 28), \n",
    "                   ('Rhinoceros', 40), \n",
    "                   ('Indian Elephant', 70), \n",
    "                   ('Golden Eagle', 80), \n",
    "                   ('Box turtle', 123)]\n",
    "\n",
    "tree_lifespans = [('Gray Birch', 50), \n",
    "                  ('Black Willow', 70), \n",
    "                  ('Basswood', 100),\n",
    "                  ('Bald Cypress', 600)]\n",
    "\n",
    "lifespan_lists = [animal_lifespans, tree_lifespans]\n",
    "\n",
    "for merged_item in heapq.merge(*lifespan_lists, key=lambda x: x[1]):\n",
    "    print(merged_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意使用`*`将`lifespan_lists`解包作为参数，并且使用`lambda`函数来给出进行排序的key。如果你对它们不熟悉可以参考文档([unpacking lists](https://docs.python.org/3/tutorial/controlflow.html#unpacking-argument-lists), [lambda expressions](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions))。 (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq\n",
    "class BSBIIndex(BSBIIndex):\n",
    "    def merge(self, indices, merged_index):\n",
    "        \"\"\"Merges multiple inverted indices into a single index\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        indices: List[InvertedIndexIterator]\n",
    "            A list of InvertedIndexIterator objects, each representing an\n",
    "            iterable inverted index for a block\n",
    "        merged_index: InvertedIndexWriter\n",
    "            An instance of InvertedIndexWriter object into which each merged \n",
    "            postings list is written out one at a time\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        term_postings = defaultdict(list)\n",
    "        merged_terms = heapq.merge(*indices,key = lambda x: x[0])\n",
    "        for term,postings in merged_terms:\n",
    "            term_postings[term].extend(postings)\n",
    "        for term in sorted(term_postings.keys()):\n",
    "            unique_postings = []\n",
    "            prev_doc = None\n",
    "            for doc in term_postings[term]:\n",
    "                if doc != prev_doc:\n",
    "                    unique_postings.append(doc)\n",
    "                    prev_doc = doc\n",
    "            merged_index.append(term,unique_postings)\n",
    "        ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用堆排序合并多个索引块并按term排序，用defaultdict自动聚合相同term的文档列表，进行去重处理，按term的顺序将去重后的结果写入新的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先确保它在测试数据上正常运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "BSBI_instance = BSBIIndex(data_dir=toy_dir, output_dir = 'toy_output', )\n",
    "BSBI_instance.index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来对整个数据集构建索引 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "BSBI_instance = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_output', )\n",
    "BSBI_instance.index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你在合并阶段出现了错误，可以利用以下代码来debug。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "BSBI_instance = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_output', )\n",
    "BSBI_instance.intermediate_indices = ['index_'+str(i) for i in range(10)]\n",
    "with InvertedIndexWriter(BSBI_instance.index_name, directory=BSBI_instance.output_dir, postings_encoding=BSBI_instance.postings_encoding) as merged_index:\n",
    "    with contextlib.ExitStack() as stack:\n",
    "        indices = [stack.enter_context(InvertedIndexIterator(index_id, directory=BSBI_instance.output_dir, postings_encoding=BSBI_instance.postings_encoding)) for index_id in BSBI_instance.intermediate_indices]\n",
    "        BSBI_instance.merge(indices, merged_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行成功且正确生成了输出文件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 布尔联合检索 (10%)\n",
    "\n",
    "我们将实现BSBIIndex中的`retrieve`函数，对于给定的包含由空格分隔tokens的字符串查询，返回包含查询中所有tokens的文档列表。但是我们并不能迭代遍历整个索引或者将整个索引加载到内存来寻找相应的terms（索引太大）。\n",
    "\n",
    "首先我们要实现`InvertedIndex`的子类`InvertedIndexMapper`，它能够找到对应terms在索引文件中位置并取出它的倒排记录表。 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvertedIndexMapper(InvertedIndex):\n",
    "    def __getitem__(self, key):\n",
    "        return self._get_postings_list(key)\n",
    "    \n",
    "    def _get_postings_list(self, term):\n",
    "        \"\"\"Gets a postings list (of docIds) for `term`.\n",
    "        \n",
    "        This function should not iterate through the index file.\n",
    "        I.e., it should only have to read the bytes from the index file\n",
    "        corresponding to the postings list for the requested term.\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        if term not in self.postings_dict:\n",
    "            return []\n",
    "        start_position,_,length = self.postings_dict[term]\n",
    "        self.index_file.seek(start_position)\n",
    "        encoded = self.index_file.read(length)\n",
    "        return self.postings_encoding.decode(encoded)\n",
    "        ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "先在内存元数据中定位term，再通过精确seek和read从磁盘加载指定字节范围的数据块，最后解码返回对应的文档ID列表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写一些测试样例检测`_get_postings_list`的实现 (1%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4]\n",
      "[3, 4, 5]\n",
      "[4, 5, 6]\n",
      "[]\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "### Begin your code\n",
    "with InvertedIndexWriter('test3', directory='tmp/') as index:\n",
    "    index.append(1, [2, 3, 4])\n",
    "    index.append(2, [3, 4, 5])\n",
    "    index.append(3, [4, 5, 6])\n",
    "    index.append(4, [])\n",
    "with InvertedIndexMapper('test3', directory='tmp/') as index:\n",
    "    print(index._get_postings_list(1))\n",
    "    print(index._get_postings_list(2))\n",
    "    print(index._get_postings_list(3))\n",
    "    print(index._get_postings_list(4))\n",
    "    print(index._get_postings_list(5))\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们获得了查询中terms对应的倒排记录表，接着需要求他们的交集。这部分与我们之前作业的merge方法类似，请实现`sorted_intersect`函数，遍历两个有序列表并在线性时间内合并。 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sorted_intersect(list1, list2):\n",
    "    \"\"\"Intersects two (ascending) sorted lists and returns the sorted result\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    list1: List[Comparable]\n",
    "    list2: List[Comparable]\n",
    "        Sorted lists to be intersected\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    List[Comparable]\n",
    "        Sorted intersection        \n",
    "    \"\"\"\n",
    "    ### Begin your code\n",
    "    result = []\n",
    "    i = j = 0\n",
    "    len1,len2 = len(list1),len(list2)\n",
    "    while i < len1 and j < len2:\n",
    "        if list1[i] == list2[j]:\n",
    "            result.append(list1[i])\n",
    "            i += 1\n",
    "            j += 1\n",
    "        elif list1[i] < list2[j]:\n",
    "            i += 1\n",
    "        else:\n",
    "            j += 1\n",
    "    return result\n",
    "    ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用双指针取两个有序列表的交集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "简单的测试样例 (1%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3]\n",
      "[]\n",
      "[]\n",
      "[3]\n"
     ]
    }
   ],
   "source": [
    "### Begin your code\n",
    "print(sorted_intersect([1,2,3],[2,3,4]))\n",
    "print(sorted_intersect([1,3,5],[2,4,6]))\n",
    "print(sorted_intersect([1,2,3],[]))\n",
    "print(sorted_intersect([1,2,3],[1,3]))      \n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在可以利用`sorted_intersect` 和 `InvertedIndexMapper`来实现`retrieve`函数。 (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%writefile submission/retrieve.py\n",
    "class BSBIIndex(BSBIIndex):\n",
    "    def retrieve(self, query):\n",
    "        \"\"\"Retrieves the documents corresponding to the conjunctive query\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        query: str\n",
    "            Space separated list of query tokens\n",
    "            \n",
    "        Result\n",
    "        ------\n",
    "        List[str]\n",
    "            Sorted list of documents which contains each of the query tokens. \n",
    "            Should be empty if no documents are found.\n",
    "        \n",
    "        Should NOT throw errors for terms not in corpus\n",
    "        \"\"\"\n",
    "        if len(self.term_id_map) == 0 or len(self.doc_id_map) == 0:\n",
    "            self.load()\n",
    "\n",
    "        ### Begin your code\n",
    "        terms = query.split()\n",
    "        termIDs = [self.term_id_map[term] for term in terms if term in self.term_id_map]\n",
    "        if not termIDs:\n",
    "            return []\n",
    "        with InvertedIndexMapper(self.index_name, directory=self.output_dir, postings_encoding=self.postings_encoding) as index: \n",
    "            postings_lists = []\n",
    "            for termID in termIDs:\n",
    "                postings = index[termID]\n",
    "                if not postings:\n",
    "                    return []\n",
    "                postings_lists.append(postings)\n",
    "            result = postings_lists[0]\n",
    "            for list in postings_lists[1:]:\n",
    "                result = sorted_intersect(result,list)\n",
    "                if not result:\n",
    "                    break\n",
    "        return sorted([ \n",
    "            os.path.normpath(self.doc_id_map.id_to_str[docID]).replace('pa1-data\\\\', '')\n",
    "            for docID in result\n",
    "        ])  \n",
    "        ### End your code\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将查询字符串拆分为词项列表，转换为termID并过滤掉不存在的词项。使用InvertedIndexMapper加载每个词项的文档列表。通过sorted_intersect逐步求文档列表的交集。将最终docID转换为文档路径，统一路径格式并移除前缀。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过一个简单的查询来测试索引在真实数据集上是否正常工作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1\\\\cs276.stanford.edu_',\n",
       " '1\\\\cs276a.stanford.edu_',\n",
       " '3\\\\infolab.stanford.edu_TR_CS-TN-95-21.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_an-appraisal-of-probabilistic-models-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_bayesian-network-approaches-to-ir-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_boolean-retrieval-2.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_computing-scores-in-a-complete-search-system-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_dictionaries-and-tolerant-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_efficient-scoring-and-ranking-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_inexact-top-k-document-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_probabilistic-information-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_putting-it-all-together-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_references-and-further-reading-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_the-search-user-experience-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_vector-space-scoring-and-query-operator-interaction-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_weighted-zone-scoring-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_wildcard-queries-2.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_newslides.html',\n",
       " '6\\\\www-nlp.stanford.edu_IR-book_essir2011_',\n",
       " '8\\\\www.stanford.edu_class_cs124_']"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BSBI_instance = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_output', )\n",
    "BSBI_instance.retrieve('boolean retrieval')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你也可以通过读取文件来测试其中的页面是否真的包含了查询的terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cs276 information retrieval and web search cs 276 ling 286 information retrieval and web search spring 2011 pandu nayak and prabhakar raghavan lecture 3 units tu th 4 15 5 30 gates b03 tas sonali aggarwal ivan cui valentin spitkovsky and sandeep sripada staff e mail cs276 spr1011 staff lists stanford edu lectures are also available online and on television through scpd sitn course description basic and advanced techniques for text based information systems efficient text indexing boolean and vector space retrieval models evaluation and interface issues web search including crawling link based algorithms and web metadata text web clustering classification text mining syllabus additional information staff contact information piazzza we strongly recommend students to post questions to the course page on www piazzza com instead of sending emails this forum enables students to discuss the questions they encounter in lectures or assignments here is a quick introduction video email if you have a question not appropriate for the piazzza forum eg one that is only relevant to your situation or one that reveals part of your solution to a homework question please email the staff mailing list at cs276 spr1011 staff lists stanford edu professor pandu nayak office none on campus office hours by appointment email nayak cs stanford edu professor prabhakar raghavan office none on campus office hours by appointment email pragh cs stanford edu ta sonali aggarwal office location b26 a ph 650 723 6319 office hours wednesday 4 30pm 6 30pm email sonali9 stanford edu ta ivan cui office location b26 b ph 650 736 1817 office hours monday 2 15pm 4 15pm email ivancui stanford edu ta valentin spitkovsky office location gates 228 second floor office hours thursday 2 15pm 4 15pm email vals stanford edu ta sandeep sripada office location b26 a ph 650 723 6319 office hours tuesday 5 30pm 7 30pm remember to carry your stanford id to open the basement doors email sss cs stanford edu announcements if you are not registered in the course but wish to receive course announcements you may subscribe to the guest mailing list cs276 spr1011 guests lists stanford edu textbook introduction to information retrieval by c manning p raghavan and h schutze cambridge university press available from the stanford bookstore or other fine retailers you can also download and print chapters at the book website prerequisites cs 103b and cs 107 and any one of cs 121 cs 145 or cs 161 or equivalent background programming experience will be necessary for the two practical exercises related courses introduction to computational advertising http www stanford edu class msande239\n",
      "\n",
      "cs276a text retrieval and mining cs276a ling 239i text retrieval and mining autumn 2004 christopher manning and prabhakar raghavan note this is the fall 2004 course website the current fall 2005 cs276 website is at http cs276 stanford edu lecture 3 units tuth 4 15 5 30 gates b1 tas louis eisenberg daniel gindikin staff e mail cs276a aut0405 staff lists stanford edu lectures are also available online and on television through scpd sitn course description text information retrieval systems efficient text indexing boolean vector space and probabilistic retrieval models ranking and rank aggregation evaluating ir systems text clustering and classification methods latent semantic indexing taxonomy induction cluster labeling classification algorithms and their evaluation text filtering and routing a note on structure this year we re teaching a two quarter sequence cs276a b on information retrieval text and web page mining somewhat similarly to in 2002 03 whereas in 2003 04 there was a compressed one quarter course cs276 the organization this year is a little different however this year the first course will focus on information retrieval and the text mining problems of text clustering and classification this course will have homeworks practical exercises and exams but no large project the second course will focus on areas like the web and xml and will be a large project course textbooks for cs276a we re not having an official textbook there isn t one with good coverage of all and only the topics we ll discuss but the books listed remain good references managing gigabytes is particularly good for technical ir in the first part of the course but doesn t cover topics in the second half of the course prerequisites cs 103b and cs 107 and any one of cs 121 cs 145 or cs 161 or equivalent background programming experience will be necessary for the two practical exercises announcements no office hours thu nov 4 instead we ll have extra office hours on wed nov 17 1 2 in gates b26a the class number for axess is 26099 additional information course information syllabus assignments faq and clarifications problem set 1 doc pdf solutions doc pdf statistics mean 63 out of 70 standard deviation 4.5 max 71 practical exercise 1 doc pdf grading criteria doc pdf statistics mean 89 standard deviation 6.5 max 104 problem set 2 doc pdf solutions doc pdf statistics mean 80 out of 90 standard deviation 7 max 90 practical exercise 2 doc pdf exams midterm doc pdf solutions doc pdf statistics mean 67 standard deviation 13 max 91 practice midterm doc pdf solutions doc pdf final doc pdf solutions pdf statistics mean in the 120s max about 180 practice final pdf solutions pdf\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"pa1-data/1/cs276.stanford.edu_\", 'r') as f:\n",
    "    print(f.read())\n",
    "with open(\"pa1-data/1/cs276a.stanford.edu_\", 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试dev queries（提前构建好的查询与结果）是否正确 (1%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results match for query: we are\n",
      "Results match for query: stanford class\n",
      "Results match for query: stanford students\n",
      "Results match for query: very cool\n",
      "Results match for query: the\n",
      "Results match for query: a\n",
      "Results match for query: the the\n",
      "Results match for query: stanford computer science\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 9):\n",
    "    with open('dev_queries/query.' + str(i)) as q:\n",
    "        query = q.read()\n",
    "        my_results = [os.path.normpath(path) for path in BSBI_instance.retrieve(query)]\n",
    "        with open('dev_output/' + str(i) + '.out') as o:\n",
    "            reference_results = [os.path.normpath(x.strip()) for x in o.readlines()]\n",
    "            assert my_results == reference_results, \"Results DO NOT match for query: \"+query.strip()           \n",
    "        print(\"Results match for query:\", query.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果出现了错误，可以通过以下代码比较输出与正确结果的差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(my_results) - set(reference_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(reference_results) - set(my_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "确保你构建自己的查询来测试所有可能的边界情况，例如数据集中没有出现的terms，或者拖慢合并的频繁出现的terms。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 索引压缩  (30%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这部分，你将使用可变长字节编码对索引进行压缩。（gap-encoding可选，对gap进行编码）\n",
    "\n",
    "下面几个Python运算符可能对你有帮助"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 % 2 =  0\n",
      "10 % 3 =  1\n",
      "10 / 3 =  3.3333333333333335\n",
      "10 // 3 =  3\n"
     ]
    }
   ],
   "source": [
    "# Remainder (modulo) operator %\n",
    "\n",
    "print(\"10 % 2 = \", 10 % 2)\n",
    "print(\"10 % 3 = \", 10 % 3)\n",
    "\n",
    "# Integer division in Python 3 is done by using two slash signs\n",
    "\n",
    "print(\"10 / 3 = \", 10 / 3)\n",
    "print(\"10 // 3 = \", 10 // 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "完成下面的`CompressedPostings`类，我们将用它来替换`UncompressedPostings`。关于如何使用gap-encoding和可变长字节编码，你可以参考老师课件或者[Chapter 5](https://nlp.stanford.edu/IR-book/pdf/05comp.pdf)。 (18%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CompressedPostings:\n",
    "    #If you need any extra helper methods you can add them here \n",
    "    ### Begin your code\n",
    "    @staticmethod\n",
    "    def _vb_encode_number(num):\n",
    "        bytes_list = []\n",
    "        while True:\n",
    "            bytes_list.insert(0,num % 128)\n",
    "            if num < 128:\n",
    "                break\n",
    "            num = num // 128\n",
    "        bytes_list[-1] += 128\n",
    "        return bytes_list\n",
    "    @staticmethod\n",
    "    def _vb_decode(bytestream):\n",
    "        num = 0\n",
    "        for i,byte in enumerate(bytestream):\n",
    "            if byte < 128:\n",
    "                num = 128 * num + byte\n",
    "            else:\n",
    "                num = 128 * num + byte - 128\n",
    "                yield num\n",
    "                num = 0\n",
    "    ### End your code\n",
    "    \n",
    "    @staticmethod\n",
    "    def encode(postings_list):\n",
    "        \"\"\"Encodes `postings_list` using gap encoding with variable byte \n",
    "        encoding for each gap\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        postings_list: List[int]\n",
    "            The postings list to be encoded\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        bytes: \n",
    "            Bytes reprsentation of the compressed postings list \n",
    "            (as produced by `array.tobytes` function)\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        if not postings_list:\n",
    "            return array.array('B').tobytes()\n",
    "        gaps = [postings_list[0]]\n",
    "        for i in range(1,len(postings_list)):\n",
    "            gaps.append(postings_list[i] - postings_list[i - 1])\n",
    "        encoded = array.array('B')\n",
    "        for gap in gaps:\n",
    "            encoded.extend(CompressedPostings._vb_encode_number(gap))\n",
    "        return encoded.tobytes()\n",
    "        ### End your code\n",
    "\n",
    "        \n",
    "    @staticmethod\n",
    "    def decode(encoded_postings_list):\n",
    "        \"\"\"Decodes a byte representation of compressed postings list\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        encoded_postings_list: bytes\n",
    "            Bytes representation as produced by `CompressedPostings.encode` \n",
    "            \n",
    "        Returns\n",
    "        -------\n",
    "        List[int]\n",
    "            Decoded postings list (each posting is a docIds)\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        if not encoded_postings_list:\n",
    "            return []\n",
    "        byte_data = array.array('B')\n",
    "        byte_data.frombytes(encoded_postings_list)\n",
    "        gaps = list(CompressedPostings._vb_decode(byte_data))\n",
    "        if not gaps:\n",
    "            return []\n",
    "        postings = [gaps[0]]\n",
    "        for gap in gaps[1:]:\n",
    "            postings.append(postings[-1] + gap)\n",
    "        return postings\n",
    "        ### End your code\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先将有序的文档ID列表转换为差值序列首先将有序的文档ID列表转换为差值序列（如[3,5,9]→[3,2,4]），然后对每个差值进行变长字节分割（如数字130编码为0x82 0x01），每个字节用7位存储数据，最高位作为延续标志。解码时逆向操作，先解析变长字节流还原差值序列，再通过累加重建原始文档ID列表。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，如果你实现了额外的辅助函数，写一些测试样例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Begin your code\n",
    "compressed = CompressedPostings()\n",
    "assert compressed._vb_encode_number(128) == [1, 128]\n",
    "assert list(compressed._vb_decode([1, 128])) == [128]\n",
    "numbers = [0, 1, 127, 128, 255, 16383, 16384]\n",
    "encoded = []\n",
    "for n in numbers:\n",
    "    encoded.extend(compressed._vb_encode_number(n))\n",
    "decoded = list(compressed._vb_decode(encoded))\n",
    "assert decoded == numbers\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们实现了一个简单的函数来测试你编码的列表是否被正确解码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_encode_decode(l):\n",
    "    e = CompressedPostings.encode(l)\n",
    "    d = CompressedPostings.decode(e)\n",
    "    assert d == l\n",
    "    print(l, e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "写一些测试样例来确保文档列表的压缩和解压正常运行 (2%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 30, 90, 200]\n",
      "4\n",
      "b'\\x8a\\x94\\xbc\\xee'\n",
      "[10, 30, 90, 200]\n"
     ]
    }
   ],
   "source": [
    "### Begin your code\n",
    "gaps = [10, 30, 90, 200]\n",
    "encoded = compressed.encode(gaps)\n",
    "decoded = compressed.decode(encoded)\n",
    "print(gaps)\n",
    "print(len(encoded))\n",
    "print(encoded)\n",
    "print(decoded)\n",
    "assert decoded == gaps\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在让我们创建一个新的输出文件夹并使用`CompressedPostings`来构建压缩索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: \n",
    "    os.mkdir('pa1-data_compressed')\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "BSBI_instance_compressed = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_compressed', postings_encoding = CompressedPostings)\n",
    "BSBI_instance_compressed.index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'P\\xc0\\xb2\\xc3\\x8a\\x80\\x80\\x80\\x80\\x80\\x80\\x80\\x84\\x8d\\x81\\x80\\x81\\xc0\\x80\\x90\\xa8\\x88\\x07\\xf9\\x84\\xe7\\xc1\\xa2\\x01\\xb0\\xf4\\x80\\x82\\x80\\x84\\x80\\x82\\x81\\x81\\x81\\x81\\x81\\x81\\x80\\xde\\x03\\xd8\\xa0\\x8a\\x14\\xf0\\x80\\x80\\x02\\xec\\xbd\\x80\\x80\\x15\\xaa\\x04\\xbc\\x04\\xaf\\x80\\x82\\x81\\xfbZ\\xde\\xf6\\x02\\x82\\x81\\x80\\x11\\xfb\\xae\\x80\\x80\\x80\\x05\\x97\\x97\\x80\\x80\\xa3\\t\\xad\\xfc\\x9c\\x80\\x81\\xd2\\x85\\xbc\\x0e\\xcdN\\x91'\n"
     ]
    }
   ],
   "source": [
    "with open(\"pa1-data_compressed/index_1.index\", 'rb') as f:\n",
    "    print(f.read(100))  # 看前100字节是否是压缩格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1\\\\cs276.stanford.edu_',\n",
       " '1\\\\cs276a.stanford.edu_',\n",
       " '3\\\\infolab.stanford.edu_TR_CS-TN-95-21.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_an-appraisal-of-probabilistic-models-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_bayesian-network-approaches-to-ir-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_boolean-retrieval-2.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_computing-scores-in-a-complete-search-system-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_dictionaries-and-tolerant-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_efficient-scoring-and-ranking-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_inexact-top-k-document-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_probabilistic-information-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_putting-it-all-together-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_references-and-further-reading-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_the-search-user-experience-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_vector-space-scoring-and-query-operator-interaction-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_weighted-zone-scoring-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_wildcard-queries-2.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_newslides.html',\n",
       " '6\\\\www-nlp.stanford.edu_IR-book_essir2011_',\n",
       " '8\\\\www.stanford.edu_class_cs124_']"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BSBI_instance_compressed = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_compressed', postings_encoding = CompressedPostings)\n",
    "BSBI_instance_compressed.retrieve('boolean retrieval')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "像之前一样，用已构造好的查询语句来测试是否正常运行 (5%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results match for query: we are\n",
      "Results match for query: stanford class\n",
      "Results match for query: stanford students\n",
      "Results match for query: very cool\n",
      "Results match for query: the\n",
      "Results match for query: a\n",
      "Results match for query: the the\n",
      "Results match for query: stanford computer science\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 9):\n",
    "    with open('dev_queries/query.' + str(i)) as q:\n",
    "        query = q.read()\n",
    "        my_results = [os.path.normpath(path) for path in BSBI_instance_compressed.retrieve(query)]\n",
    "        with open('dev_output/' + str(i) + '.out') as o:\n",
    "            reference_results = [os.path.normpath(x.strip()) for x in o.readlines()]\n",
    "            assert my_results == reference_results, \"Results DO NOT match for query: \"+query.strip()\n",
    "        print(\"Results match for query:\", query.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请追加压缩前后的文件大小截图 (5%)"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
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zyUyTtgtKxt/XYQ1oN8ZaOqBtQ7Sk49/8yI/YzL828i9m2rXNijbW10y5TqtJX+vDQf+D4xM31fnRoEyXXQM3vzz6WhvYV3Vl3eY3fM9f/qs2SNTqZPqZd7373Xa4BkT6WV1nWnVOxyn9jv6TH7Kv69KARwOr3/f7l6VrWb4r7F/xy36x7c3OL7++1sBPX+vny6r2AQBWEfCcH13XBDwO0S8CBk9+sHHA4xUDn9/zZN+NaebKz909a717vv1f/3Hz9re/RV7JLGyvbRoA+b8apLj32XHZ4cXpc9MtX2vA84Wza+bLvzpprF9FM8qaiecp+/tDS5w0qNLe13wJm24nDey0IwftaltdfVdyAPSiR8xDDz1kXyutEvfidz9vAzJ9AKvO7xu/6VesbGOteqclUX6+Gmz923/7427sKq3qqMHkU9/5XW5Inj74tE2pFgBctPPOgHc9n0tAUxMBD0p84FuOzdd8zb9nph9/pVHAk6UlPBrw/OzP/jvzyR/+n93Q+jYKeszd/92Yn/l7Epfck/1cAx8dmJwa7svrK1euyF87Rlyx/+l7GWH/XjEPJJ9zw9Pp7SRJIZR9/cCD5i1f+9vMA+9c31UxQc9+0YBGu9LWKntaguVlg551ssHL5z/3ueD8VDHo0dIjX0LVBvsRgEN0nhn05PrebQQ8AZdhw6NzNgt69hBBz37RNjm/f/Ah8/f/yT/LtedpEvRo9TdtW+Rp6czfmH5spWvuYtCj3/2FN95wY5v76oce2mkX5gCwbbvMoF/GfC4BTwmCHhwggh7snHZAUOzAQAMU7T0u1MV2W/o9+jygbc8XAA7BeQU8XSxJIrhpgIAHB4qgBwCAA7frTLvPKFxEcLDrTAoBTwMEPDhgm/XeBgAALtQuM+2ax+1yPpeAp4Tf8MUEHLDOlfQAAHCZbCvjvi4zcFEBwi4zKQQ9JcgZXnqbHBv7uvtQ0gMAAGwmpypdhG1lnkK/RxMCCHjQku46+7z7EPQAAIC9ss3ME8FNA2UrvRgt7jIBO0LQAwAA9sY27xSThy7ho8piCjnvlchGO0hlu88+IegBAOASqJO/BXIIQC6t4vliXToEBD0AAByoJnlSnTab9lHbzFPxt/mEEnVWDisQHUPQAwDonGzG16cu6dpv2uRucde27bnxO1FZAjqGoAcA0Cll+bWu5OO6lh/dpGoMefMKh1LnCDgnBD0AAABdQsADrCDoAQB0Rtmd/02qT+2Ty1rKo787lFCCFQSsIOgBAByEbGa3LIV0Jdgp+32HwAedxVTHIf9utFB3xwAaIugBAOwln9HfJMN/6PmnTX77vthkGxz6b0cDTSJhoAWCHgDA3iGzyzrAJUKwg5r8jaA2iaAHAHDw/E3iYjpUeoHuimLGo0nCJXDIByrO1abnhCt37txhdwMAAADQWZT0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOu3KnTt37rvXjXzlK18xX/rSl8z9+60+jh25cuWKedvb3mbe8pa3uCEAAADA5dY66PniF79o3vOe95gHHqCwaJ+8+eab5rOf/ax5xzve4YYAAAAAl1vriEVLeAh49o9uE0rfAAAAgCWiFgAAAACd1rp62927d821a9fcO+yT27dvu1fYNd+G6uzszA3BeWL9YxPsPxeDtqcALgJBTwdp0HP16lX3DrukVQm/8IUvmIceesgNwXli/WMT7D8Xw6932p4COE9UbwM2oHcsaUN1cVj/2AT7z8VgvQO4CAQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnbY3QU88m5lZ7N7kxGZ8fGSOjgZm5obkxWYmnw1+NCXzGByb4+OyeQAAAADoqv0IeuKxGQwGZnBybAbhyCcsnpnB8Yn97MlgTTgTL8xi4V5nzGwwVDOt+w4AAAAAe2c/gp5oZKbToemZhQQhJza4WBf6xLOBOT4ZmJkGMr2hmU/7yYjGNBiqmdwnAAAAAByOvaneFvVH5nQ+Nf2ee5/8KbWYzSQI6Zn+dG7OTkdrp6/WM8P5mX0qdzjJcrkpAQAAAByWCw16VqqWDcYmlgBEq7sthw/MxBaxaFW25bRjVxQUjweZaSWlVdCStj4+JaU0cdJ2yKW4rDhJRpSO67jFqy+Yp556yrzwanm51uKFp8xjjz1mnnph07KvhXnhqcfMQw89ZV51Q5C3eFXW9UOPmY1XdamFeVW297ptfuksXjVPPSbrpHKVLGSyV82rst7qrjl7fMmxs26+L9jj69Xa8zVyBOl8133mVTneHlv7uzJyJdzJ8frYU+d5tOq6eOhAzxGyTeTYbbYdG7L76UPmsV2cINy8z3VzA8AOXXBJT71qY71eL0nufUKHuZdWYV7xzIy1nZBNk6QanIydpMN8MFUkwdXJiTmxAdglJBe6VzUjV7lRFub1119fu93W0236uvyViytX1iDdxV+X/178wG4Cn1ef+oBkapJt/uKrui0uE5+hXl23Gpy8+rqskxer9kvZLjZgfNHuxXXo/v6qHDsvfqA8E683FV6UaTSYqk23oX5GFiR/nszS7Sx7k05TPlGGHJcf+ID5QPbYXOjeuCpXDViSPYdIekF+iwbUepPksYdkXT/2whbOGwdC14Hd3lXbZEOyj+r8X39Rj2M3rCXZbAWyLWXeelOkdF/V7e1eA8C+24PqbT0znJ6a09MNk20TlKHthOZzM7dpaoZ2ZD8zbG5aNwNCLcWMUCg9/PTT5mE7sVy8A+ND6VJ5/CXz0uP6QjPY280wLl54zGWUHjaP60bQUqVdRFYHqPfMS+Zpt062Go+n27Mk0F+8YJ56UcOKx81Ln3ymdmZZAwz1+ON25mE2Ey5kmoqpmpHl1WDmAxocZZIGOppelKBRl01vkrz+8MOyp4WDpu6RgPqFGttkjdD5L5ceflr2J3sGlfUs54fQNNlkpyyQffwh3YbFAL/3jHnG7asvBM8LWkos23unJdEAsD1X7ty5c9+9buTu3bvm2rVr7l07s8GRGcy0Pc2pGZmxGYSLXtbqDaf288cnE7PoT83ZSjSj3V6fmMlCgp5C+5zcMtiGQTMz0O6xtXOEtK2QGxac9/65ffu2uXr1qnvXjGaEPyCZroef/oT55DPhLFedaeyFdKu5xaXHX3rDZRz3w+c///l261sDvVo5QK1W9KrpPf2MeXxdLvhhydDWyCn7bagBz9Of+KR5pieZcFeFqHK77qHW61/X62MfMC++7teBG+yl+7AEIG+8FAgU/DoLjK/ctpKJfGoR3p7yuRc08/n0S6vL4xW3sQYeH3hRgoqnzScqAqVXn0qqKj0u2/fpsuMnV6Lufp8Eam/YA86tr55/r4OS75aZSgY58802wFHFEvmm/DYq2waba7//CA1SZaWuXrmSEjUrXRdVenJeK2zzHZxDg8d21f7jxwWPgYr9vwZd7+985zvdOwDYvQsNeuJ4ZhZytej1+yaaDcyRbY/T4CLp7lz1p2dm2tc2PDqznulHxW4NCHrC9O6fe+m8rne2bUCjd6TDl+rKaXymSWas1W124WHJ9G2WkdqutpmmZeCxPXUCltWAJxmuGe6nXLWrh7VEQjK2e7SaS+0s6EnHl63X8kzfLrat55fFBzFV0hsEaeZ1jTTAUQ2Cnhr7XU5psLBKS4mstsHDGpsGPbXW61qBfdAHPTVvZFRyQXh4P64+Dvx+tvLZdPmqg+0yBD0AztuFBj05LujpDefmNIk+1nOfSYIeNyzIBT0mG8gkSoOeXIDTzaCnTqapsVymaUmrVrS36d3i3do06KkKMGt7/UXb9qI66Flm4oOZLKWBj9bh12m06sxLNUqXLtjugh6R3m1veqd79YbC1rgbC2lm9PFAcOoyuT7oSfe1shsG/ibFOQY92wkWiiq2ZYmtBD0tM/7pOg3tg27fqz6m61mea8Lzqhxf8hvXzXMdgh4A521vuqzetnjmHnia7chgMct1ZDDwXcBdUpoB0vrmueTz3qFxPlVN83Ag8y4XTdsgunXSjGWXPSwZUQ3s8kkzPfr7NWMbGp9Lbk5ltGH+Yw+5gEe220tlGcOejPvkJ5L2LBJIPfUBbfezq9z7AXj8aVkXD8ufx2uUMmQFttEmKTtP+9rTZdNSuXx6OhdEa4cMuuEfN898cnVamzbMVDfWe8Z88o03zBuVqRBIaqY7OF02NQt4kOjJudse8q8GSt/0nPDSJ8wbuaBOO6uwJxM577PCARyGvSvpaSNU0hOPj83JujZC/amZGgl+siU92l32Stsg2vRkNb7Dl7lT2CxzJRfWF140r+6wPv82bF7SE1qPvhSh5p3r0rvC2thYG+O7e+q170gXP6cZn6fN45Lp3jc7LempVFXSk9Aurd0arFRdZdN9T2HbJSU94WX3+5aW9Dy9qHG8+hKtUEmPvHrY3czwVc3sew1cnl64Y/vxWiWCWqpZdz0nv0G/a9k+pvY5p4G9LenxpW81120lV/JXPi+/HMvSwUob/25KegCcv/0Lenp90y/UbtNn6+iDSHsyIjcqjs1MLgzrq7eF2ukkVqq3BavZEfRktQ56Sqq+lfMX4m4GPVX8Os5nQptIAkZtFJ/kFx+WWb0kmZlm2RMtIXrRBp5ugGSann6m0Gj9gl140FOa8VtmJNdZZjRD+/wGQc/TT5vFi3LsVQRmVs2gJycb9LhB69TKUCt3ztAOHR6XZdP18bR8l/yUltuq3LaCHm0DF1hLa7xuby6s7IMSpCyP3W3SksFwldUm55zSdj4NEPQAOG/7E/S4AGY16CnvhMB/piefWem7ICstvZGgZ1od9PgSor4EPUMNsuzEBD1ZrYMeeRnMPFVI7ixfsqAnXV/tf3eagREPP/60zOPVNc9eqtKTTJIEURL5yFbfeqZzUxcW9KwN5t38TUWG2LXF2lnQo/N9WLa9zOlx84J54XXfDXFBVdBTlQlOgxM5F/RetPMIBTbJ8tQNWLLrQIK29LXsxZVBZjtbCXrc2/aKQc+25ltUta/7IHfd8eCn2+y8TNAD4LztRZueWIIXbV2T1FeX1/o+TZmcWm64JBmkn5ER6fsgCYzsXGYTczKYlU8nfIP72eQSP6B0VyTgSdso1EzN75weBs2w6rMxginN7GjmIjB+XZKMp33OzOOPm6dfkszoS5pB1CpCLZNshcdf+qT55Cc+Ydt/rM+0XhJ23dTzcGDftsmN36meVmmSfUn2qxefqn6mStObEjnyWf30aqclScCswbP85LX02S9JgUMxQy2ZbI2mtK3Z3j0YRtu2yHpukYJrvFabpyR9wjbAkyWQwDM0Pp+qghktTdN5vW5eLD6zJ2PxwgtyZhK6/HYIAByGiw96ZgNzciIBRmkamKRpzswMguMzqaRNkAZESjMZ9vuOl8GMlhL1tcts+25mZrbXbBmWuzC4h5oeQCnPXpJM1zO+wXSr5B5g2imSQdRMYjG5sSo43ic3jQqP69l17quh6d33cCbIZyz1rm1ovCR/V12OHxotL/nMva7zaq/bB3QG00qA0JTM+8XkQaDZ9GJaH9FzAYNmaHfVMYgL4jRQztPgWf7UyCRriZAWOGmJU7BwyQ1//cWkg4+9odU+V85bddLTy45hKkng6G/e7VDvmWeSbfSqtuezg/IW/uG5D0uAtG5rAsB+2Zve2/QBo3MNLFbS1AxtPssFHqE0HSaZsiD3/B75/Oj01Mx1ZotJGvhEo6mZTke2Sl08HkvYI8siQdDK/CJtT1RRmoQgbRPy1FMvJhm8F19cyZytSy/azKF8ftdX+3NmS04+WUwSgLgMkAYpq+OXyVdRCk4XzC1iu3zpRY3eq15/1bwY2LdtclUQN6E9bq0EU6HZpoHEq+apx16on4HWjG52mV8oe76OBt3yp5g51+WRP2uDQ8lo2yqZWn2tYh/2JUCvrim16hQ5B9oeMHce6fnSHl2/xeB4YV6Qc7nd6xt0SAEA+2KPuqzuSVyhbWiKKXNmDY6XVHXyjWdJd9USyGg5TTQ6TTo9yAQ+Vjw2A1ukJMHRqGSGMq/B0ZE5HrfrZW6f6B3VUNUo3w5E76SGxmtaN81j2ZzI4nV7R7x5ymfiVm4ed9DiBW3QLC/K7nJ7kgmVfKfQ6jF2CHZF9sUg3xuWnLfWVtnSRu6f+IT5RChtHKBq+4vV0jlf5ano8Zcy3ZFnjlM95krJweePy+R4zEzrqvjZUnRZF49r1C5BXnYS/Yzuq89U5ZIl4HnMZujl99jqmFUel/Wppb9aanU5Ah+/faoDxzXnWjfVOrZqrP0aCY4zQZavdmj3ZyIeAAeos8/p8WaTiT3ZaxU2rz+dJ6VHCwmIbNSjVefcdNNCZwlZ6YWjCyf8XlIVagcpu3b0ArpSElGatDpW8qDFZZCjvQ3ps1Lc267KVBuxGcdSy7utq20eShQCyHzy+7T8DY53Sfb9y0Uy+E89ZttHhX65llom+T/tJGK9C23Tk6PVHpPqoq+/+FS9gEGCcH98rjsOk+e9ZNqE+AC9qmqbC3iSfbqqzUmGtnmxAWMS+Oy8AGSdqtK8ypTpGbGCrzKo+0wZ3Z6rzzjLpNorSfYRLXXWl7ptZCdJqx3K1l0flALAfjrMoCfWKmuztEODsQtsVi4IM30Gj/ztDc0wF8lEZnSqVeO0xzYJeI4HtlqbfW5PacTTLc2CkWap6V1AvaA+ZkuJPiAZf8kEyCX16adfcg8i1PnJRfYSXGV7NkOpXdh+wDz0WPihoOnd1nWlQRlJFcNQhkuT7zRBMo/B8S650r3LQNeXPsjVPqPIlWTkpCVtSWa0TtzwevaOeza58edKAoaXntbSkjUBhls+vZFRRn+DZoTTSWTetuqlrEMNqJLgsLz9xyIX8NTsztqTYyAp0dJjplDCfO4q2m2tSeuPLJ1O/64p2dVn8GhwWZYqtuOqpA2YXbsvSsDkjv/aQSkA7KEDLemZmfFg2QHCxEYsPdPP9XUtwYzr2KA/yndTnYjkv7E51q6o9Vqpz/ApRjxpSZBKAi2lVeq6LMmIlNX51yoPL6xvY1NZulBMScZSu1a2jYEl16RBTrCtgqaLzNvsimZEPylB3ifc0/QlM22Dn4ck+LFtKLTkIXk2hmZukkbp9fQel3mnDaeLyXcSoXdwQ+Nd6nxRm9Bnozz1mPmADwRttbRiadqypM2SY2XtHXStSha6866p9t337dKbHqGmSFUlCSGrnRZIxvhpV/VM1qX9ebL/hTLKthtrF/Boz2MvPZ4JBtOUmf/KuIX8jk+mVflsddvHapybdkHbIRWqGNZLrrphFT3n2Rf6m+2LID1/Bo9dn5pGKxJUPpMtdZbfSN8FAA7ZxT+nR0tt9EweeChpIvScntjEEqksz/+9lQeX+uftaOlN6Nk6sZYCSVBk5xF4aGn6+aLAtPum/XN6NGP9VHKHW6w+h0fH+16TtBtjuZCWNOK2GZodlQ7Ufj7QOdn6c3qUHBO2V66Vui8S8Gz1mUXaLfbmz9y4SNt4To92RqDV+9yeX7JvZ/d/LSl53LzqekJ7WKuArQSibv4X/ZyeGhvVB9S56SWgyz93Z/l7kuUovl9KA/SS/UpvrPiAzx/Pbc4ZftmW89PtEg7oymzlOT2FbVPfch8sezbOcl2KwPf49bbV86K9AeDaGGZt8eHEPKcHwHm7+JKeKLLtbcIBTxkJcPQzacoHPEp7ZRtKIDUtqa8W6QNN5W9PgqJQEKMdHmivcMvvkKQ9zO15wNOaXOSekotvksnWdjShC2hPMhl6R1azb74almTGQrHhMy+FG24HUpKh0pKG8PhiuhSNaHuyrp/WjHcxqyyZEbvOAysdDbmulHVfdgGPBi+f0CqVgYBHM6dJ5lMzqJqx1gy2ax+jpaMV1avqt+mR7S7Hzj50064lKfpbS2tFaWmurrTi78hU/7P7a2C99GzVtMft+cSfZ8Ilkr5L53BJpC8l0flpKWnTgGfvpevS9armguTdHf0a2D8mgZwLeLRUWTvK0PWq69q2XfLnfc5BAA7LAVRvi8xoqu1vKjoYCNJ2OxWfibTk6MycSlBUFsRE/ZEETdqltUuj8mkPV3KRe0jvWKcXuap2ORr4fFIugi4zIhfBpz6g9emLF+JC5q4ipR4Oj19JbvKu0p7rnnpMt0kmCNVgUzKJdpXbdZ5kPF7oZF2/85HrklfvYGsGXCLwlf1LMp56QyC56124I582qNf86Ae20q5EOwPQkqd6+7k25F/tQbFZiYmvNpUNcOS8kEQ0cswlQ4p8Zw7aXiQhn3lB91vXjkeCEh1TVu1Mb4zkgkt9iGq2DYpLySLJcgTG5ZbNPoTVve4ECbRdVUrtMOMZWV82yNNSrZKbTa1pybJeB1xbNvtAYm1XqR0a6DrVAP+Ty3NQct7X7eqr3wLA/rv46m3YurrV27RKiHZJmmSPkotcs1IUvVBmqmDZtiZN77Sur96x7zau3qYZDslEvCqZh1dzbSR0mzxjntY2Tm6Irq+FPrMj3W5KgyJtC5TPKKddYFfS+SWZnIcLnw96eP+6q229/l3VJFl5st+Ggh3ZLtkqhhX7d7ZqVnl1sFVl1dA0E+mnT6tuBau3yQuJVNJYxZP9SJcmN19fMqPbOvMB7fUr+Y3y+3xVNF9tS0tQljNY/p6XjGR6l9W6tOOCF19wPZHZANI9x0UDeA0u3feu7s/r+PNDZtm2bCvV29zb9ornPz23upLF3HYvnHPttkxKLLVNT2W1M7f9l9O5c4mcd7KnHS3t1OCmLNj1y/CCzCz/u3X7ajD6TK1rANXbAJw3gp4OqhX0SEZK6+srexGUzEidC1WIfwCpvQDmMkmr0vrnPtflMme5DNeBaZtpKm3DoJnGZ56WzENVEBIIfmTd63Nf/GdybQG2Zc32vQibZFptY/iV3J1k6iRgfEG2jV+3NiOYWbchyfrWAFSrx+mQUNCjw5YlTL4jgHxwEs5IF9ts+O+r3aZnTQY9O/9wMLb8PU/3XjTaK7V+5pnXfbU/XU/afqkY4OUz6sUAr9qhBD2y3VueQJO2ZPntuDw3lGzfbJDZiJvf68vzf0Jvemj1wmYB6auyDC/IjrBcjPDyhhD0ADhvBD0dVLukRzJ2r/a20yhVL4AvyEX08XXPcLBtHwp3FiWT/0zjEqL90TrTlMkwPZxW2alR2pLjg5+FeXwlsyHjtl3vZA+rF26UaS3jbgo0uyGg61vWTzptKOjRWRceqCmZzU/afp49rd6Y7z1R27sUS0iS+Wh109Wn49tMseSI80/O12M0G/TI8soce7L/2Tv0ueBPl/1F0/PPa7H08xqwaYlXz7wo+5x9Zovux09JgLSmgbtdpoV8tk6OOOXW4b4HPYEOBurxv68YLMg+8NBTRn702gDRtr1Kbx6to+ea5Bi2QbNsj610TLB4NQl+9OZAzY1E0APgvBH0dFD73tvQxk4y3ahtr9e/ZEhtYLFhnvJS2/E65Pi9GAQ9AM7bgT6nBwAOgJaMEfBshnUIANgCgh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0Gl0Wd1B2mU1AAAAcNn4xxDcu3fP/vUIegAAAAAcvOyzKotBD9XbAAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdNqVO3fu3HevG7l79665du2ae7dfHvzf/+/mwZ/6PmPuvWHu31/+PH155coV+zchr/WPHyDjdEAyzA4w97860hfGvLNnzNf/cXP/q742eQ8AAABgb9y+fdtcvXrVvr53757963WupOfBH3nOPPjjf9EFPDrkAfmrgY4ENBrEuKDGJvfmvg1+JL2ZDDc6vby3QdAXYpvu//TM3P+h327u/7sf0CkAAAAAHAiqtwEAAADotM4FPVd+9hX715fo2H+TQh5HXmipjyv5yYxI2FIe/Sv/26KiZBqtFme+8oa58r/8KXP/jVinAAAAAHAAOtem563//OttsJK23bHtdJKfaKus6V8d74KZZWBj45zkr7xIPr8c5+mg+0e/zFw5nporb/86NxSo9pWvfMX8wi/8gtunAAAAuufLX/6yOTo6Mu985zvdkPNV1aano0GPeyNsWx4ft2jA4v5qUPOmmzAJgNw4fS3DswGS0mnS8eLNqx8wD37LR+1rYB09Xr76q7/avOUtb3FDAAAAukXzO1/4whfMe9/7XjfkfBH0JHGKjWrsKPmHoAfn6Y033rAnAFtNEgAAoIO0VstnP/tZ8773vc8NOV+Xqvc2jVE0OEmCFglVbPU2OyAJWuw/8sf+TabRaf30yXT6OpnmypUHbEo+llR5s8M/9wnz5v86tgkAAADA/upcSc9b/pmW9PhgRiSRi76yAU0yXF9oAKPvM+T9fRskJePTeSgZlrTzcaML8sPcZzMDfSCVfPVyXK4ESYZlX6v0vX5cu9T2QVzWWx8yD/7S322Oes+4Adg3lPQAAICu2+eSns4FPQ/802+wQYHGDLYEJw0kHH2jJTc2qHAZ0MwEyeDkc8uARLgXNp7S1w++3Rz9km+zw976Nb/W/k0e9CPJkhVtZ6b0ryYdL3/tNH46cV83Sma8vrbDlJ+++Fcl091/88vmS3d+1HzpHb/eHH3zH3XjsE8IegAAQNdRvQ0AAAAALkjnSnrMP/kG8+abxryphSD3r5h79q8bJ2wJT1pcoyU5SalOQoa7UbakJy0R0lEywr18yzt+qXnom3+veeCtR8mAN78oyZfW2A+7116mBEelJTXKD5eULof+ddNkh2VLeOwfeW+XN3n/85/9t+bB45fsa+wXSnoAAEDXUdJzjr4iv+/sK8Z84Rfum8/dfdO88cX75vN3k/S5L7wpSV7/vL435o59r9NIptSmN83nZbwd9vNGppH3MlzTF37hyHz56BvNO3/lU+Zdj/4R88BbHpS1+UaSbMDjAhtfBc0HKT75YTaY8X9dyk0j89Kqbdlh/r3/XG7ccvhXPbSHQSgAAABwwTpX0vOF7/8G87ave9y842t/qwQmb0/iARtcKA0SdID+9UGDvs+Mt380iMlOp+Tv/S9LTHImf7Vkx33e0unc9Onn/GtVNkz/+GGSVl4rP0z54YFx7vd86Rv+bPIee4WSnoLXXjOvyZ9HHnkkeQ8AAA4eJT0A9ttrL5vnn3zeBiK797J58v3vN++X9OTLbhAAAMAOdS7oOfqGG+arv/bbzAP3Pivh5r815uzHk78+nfn0E0n6kqafWqazn5S/P52kM/37My79rDFflnne+4IrVcmktNRIX+tfXwLj0rph9nN+HpnhuWn9NG667LS+ZMp+DmjmtZeflADkSfPcy6+Z2A0DAADoks4FPW+/9qgxX7njAoFMMFGVfEDhg4rQax946PTahicdJumKpDQY0b8+6XufdHpN8trOxyU/T03Z4cGk07jp7LT+N2Y/C9Tz2svPmyfff8W8/8mXz6mEZ3decyVVFBwBAICQzgU9DzzgSz00AJC/NvmAQFIaMOjfbLCgSd/76f1nfXLThOar7XvS+chwP43+9Sn9TMVwGzy5ZJfNpfS9Tp9J2WHpOKCaluxo26L3P/mceVminYNvVvPa8+ZDrqQKAAAghDY9wGUTu0psjzxhnvv4p82nn30ieQ8AANBR3Qt6st07p6Ug2ZKS7Ovs++Iw+Zu+d6/texnnP+NfZ4f5abKftZ93w7LzsNP5qnKZcfZvJtnv8u/d+Ow0/rX9i254zbymPZxJqs1Nv/Yz0bPm45/+tLn/6Y+bZ5/YfTGPX6bG5TB1f09Tu5ovAADYWx0MenwwoUGA/s0EDD7wsO/d8Gzw4Idnp0nn4ZMM9+PtU1D9ezfOT+c/5z+bTuNe+7QyvRuWTqPjXZU9Ozz72iX7vW56HJiXzZNXrtjqZrYns9e0ZzN9n/RupimpilbS7sa2ZUmmueKmX34m3BvbI088YZ7YeZ2219Ll8sv0/oplSjX4Pa8976d7zg1frktNuZ7hWqwnAADQHR2s3uYCABsYZF/799m/hZQLOvxrn/w02c9mxqWfk7/pNG582Wfs9Jr8MJeK0/tp/PD0tUu5eeBwacDzZNLORgMTST40SXpYW82cv/y8b8vySPoZH9C89vJzwc/sngYf71+2sXnE/RZZLL9MZb3E7er37Od6AgAA54U2PcCeePlJCXgeec58WoLYT3/84+bjkuzr51zo89pz5vmV7skeMU98/NMS9346/YytuvZx107ntZfNK+eam3/NPK+Bm339hCzLfVuNzv4W+/o584hWLbPjQ+r/nkee1encPO0Q+T5975L/SGLf1hMAADhPHazepiUfPmkJiCZtN+OqhaXDs9O5lC1ZsaUmOp1PmWnS1557bz+nKfN+ZZgkS/9mk04rf+14994Oc5/VlBtffO+mxwHTIOFZl4Ff0sy9z5u//Hy+REIz8h8Ptct54lmTxEqvmZfPMzf/2iu2pEqDjOck2FlZtEeelaAjF43k7Or37N16AgAA56qDJT0+858NFuS1TT5AcEFCGpC4ZIMIfe3Gp9Nmkp2H/4wflnm/Mg9Jdpi+d8NsEKZ/3Xg/LH2tyX0mN62bR25+mfd2GA7VI889K2FPmFbHsl6r+wDRi3nM6GuvuLZHjzxhPhiIMSytWuZe1rer33Mx6wkAAJyv7pb0+ACimHwAYadxKRhU+GGZlL7307iU/Zwfb6fPDtP3blg6TWa63HhJuc/6YW6a9L1L2fc4WFFUFiUIGZeMjU1cLJDQRvrPP2metI3zfUP+J81z2yy4eO152xGB7yQglzI9BsS+R7QoWimxqm1Xv+c81hMAANhLtOkBDtjL2iOZPpjzuZfNy7atjARHvuMAN812REnj/1B6JHLTvLZ8BFA6rJld/Z7zW08AAGAfdbd6my8FKZaEXCmOz5acuOG+dCWdxqV0mJtH+l0upfN1f/18/efs6+w0mnxbI0naNbWdTl/75D6bnZ//rJ/Gvtdx+h6dFPvG/5EW+ljaZfOTrkeypJG+Nur/tPm07Tjg2dX2NJuQAOFZ3wFAMT27nS/a1e851/UEAAD2UgeDHh8gFAICPyz72qbia+Wmy43TYf61DPfzyc6r+Bn73S6l7/14N206zLXd8fO2z+bxQZAb5lP62ex7Px8cqnil3trSa8siFAl71GvmlaTHAPPIcx8LN9I/d7JsroDntdcq2srIuNWxu/o9+7ieAADAeetgmx4NHLLBgwsIQsN80OBTGji419nkx6fz0GEu2WGuxMZPm87Hvc5+3iY3bTqNe539a19npvGpdP6acKhee/kVV5pT9LJ53jc8CbSVCbYFSntRO1+Re/aNefl583zJ96edHZTY1e/Zp/UEAADOV3dLenzwYIOQTErfu3G5wCP7NzNd+l5fu8/4aXPT+9fufS7pOF+aI8lPm/uMS8Vx9jPy134u81k/n3T++hoH67XnzIdWIoXXkuf32NePmOeeXe337OWXi0HEa+b5Dz1XGVjsyiPP+h7oXjPPfWj1gZ/2Iatreg5o/3tels+6lwH7tJ4AAMD5oiMDYE9oo/rXnnu/uXLl/eZJCXSefPL95v362mXktT3KsvnMI+aDvqqWBhLvz3/mOQk9LqYm1xPm4+kDP5+zPb693y6XLqO+ftk88pwuXdEGv+eRD6bjtMOC5Pv8etvX9QQAAM5Td6u3pc+30dIPl9ISEk1uXDZlS07s38w4P9ym7GuXsuOzpTF2eHZ6ef1mdlxgWj/cT2+H+enkd2lnDJrSThD0t7rpcLie+Lh9cOcj5jVbKvHyy67zgkeeMM/Zh2vaqVL60NJPP+d6H3tt+RnzxHPm04GHnJ6b9HckXrPL9bIs4iOyaLLMz4Z7dmv/ex4xz37sOTde1pn/nLO36wkAAJybK3fu3NGcdWN37941165dc+/2x9t++nskANCf5H+WBgWZ1z6l0/ikfzLvc/NQfpgPLPx79zr3mex7Tz/nh+nfbICSHaavlRufnWdu/vonsyx2/JvmSw9Pk0HYK2+88YZ573vfa58Nk/eyefJKUn3tiY/fTwOb1yRzntCuld3LCun0MvFeZeJludJfUueHOG1/z7rP7e16AgCgA37hF37BfPaznzXve9/73JDzdfv2bXP16lX7+t49LRxY6l7Q81N/Qf7VAECS5i+LwUUxcEiHKZnWviwEE/Zl9rWOz7xX2QAkndYNy32nS+l36l/93sz73DL7cS6lr5UftvzuLz38Ufca+6Rp0AMAAHBo9jnooU0PAAAAgE7rbpuetJTHlYTY5Mb50hM/3JbI+PH62k+TSXaY+0zxAaf+8/4z2df2fWh+Ltn3xfnpa22no8mN8+P9dxen9/MCAAAAkNO9oMc28HcBgQ0MNCDwyQUG6bDMez8s+7l0Xm64Hef/yjCf7Ht9qe/dNDb5abLBixuezi/72iU/PB2nxXNumH/vh/nP2mE6DQAAAICsDpb0aMbfJw0c/F8XMPjXNmDIvM59xg3TXtZyw+WvDTDktQ063LT+vU362k3nP2u/x7/WlPmsHV4Yb/+6+fnxuXm6z2bna/m/AAAAADza9AAX6gnzcQle70uiEwMAAIDd6GDQ40tF9K8rKcmVkmgJin/vSkpy7yW9qdO41z7ZaULD9HPZcdnv1Gl0mH8vqfhZO0z/+mndOD+dHeeWObvs/nPpvNz3AgAAAMjpaPU2HwjoXxc4+IDABxZpQJF5bx8aqsGDG5dOl3lv5/uV5K+fr52HD3bcNHZaDVJcoOLn4ZfLf9YPs38z09rp/DidXoe54dnPpe/deAAAAAA53Szp0VTay5km994P86/Tz+j7zHA7nf/rprHD9a++d4FSOp1/reNcstO7lP1+m4qBkc5P37vx9vNuWPZz6bRu/vYzAAAAALJo0wMAAACg07pZva2YgqUtfpgrKfHj7V99r68z09txmWH2M5n3/jP6+oobnh1v2+PINLY0Sd/rX0nZedn5ZMZlp7GlR/pex7ukw9PnEbn3AAAAAHK6W70tGzzYoEFTIcDJTpMb5oanr3V44TP+dXGa9HPFaXSce23bDvnhmnRaN96/9uNz0+lrP16n99P5YToNAAAAgKwOBj0aBPjMv3/tAoRskJAbpqUwGhC5ae008j7bLsiPS+eZSWnAoa91XvLX9gCn7/1wN5/Kz+prtxzZz6bT6l+/nO69H5YuPwAAAIAs2vQAAAAA6LRutunJlci4EhZbyiIpbVPjxmenSUtd3OtcNTQdli1RKYzLfl86LJN8CdDKZzPvi73A2eQ/X3id+2x2GgAAAABZHQx6MkGBTdkgoTDOBxs+5ToZ0L+ZcZrsex2tf3VeLq1Mt2ZYcTnSpMM18HHBTzpdJhiy35cd5/9K0vkDAAAAyOlmmx4bXPjgoBhA6GudRt+74f4zth2Om85Pn85Pk59Wx/ngRD/jxvtp/fTF93aYfFa/x35vNvlp5bVfhnQe7r2fTzqtJp2Xf63zAQAAAJBFmx4AAAAAndbBoMeVlGRLPnKlJppEtuTElqzotJq05MaV3tj32eSnd+P861rvXfLjsu/9+NDrYsqN0+/QeblkxwMAAADI6lzQc/8rZ/KvZv59MKCvfXKBgQ9qbJBQSGkQoa8zyb/PzTf72id5bz9TqMKWnY+mlfeZ+djh/rP+tRvnx+c+mwRqb2rHCwAAAAByOhf0/MLnf0YCgEyQYZO+d8NsAKJBhCZ9n52u8N6P98Ps62xJUGacfZ+ZZ27+GgC5YX5Z7Hh9reP9dJlxLpBJxuvnC9Om89a/yWfufvFI/gIAAADIok0PAAAAgE578E//6T/9nHvdyJe//GXzjne8w73bH1/+/E+Y+5+bmweP3mmuXLli8qUqmdcr75UOW5achKfX8fo2M51K3ys/bSal7910pe+zr5W+z3yXfe0lr99885753Ge+bM6u/k7zVb/k19th2C96vPz0T/+0efe73+2GAAAAdMuP//iPm6OjI/P2t7/dDTlfX/ziF+33q/s2P7105c6dO/khNd29e9dcu3bNvdsvP//pF8y9n/y75v6X30hCB/8LbRCUvNT1oEFRdrx/7d5aV+Q/pcOW606H3S+8T2SHpS/1hQvA7PjMcli6HMkI+3Y5j4S+t8taMvzKW77avPNX/R7z0Pv/kBuDfaNBz/d93/eZz33uc24IAABAt7zrXe8y3/md32ne+ta3uiHn6/bt2+bq1av29b172jxkqZNBDwAAAIDLpSrooU0PAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaVfu3Llz371u5O7du+batWvu3cX46Ec/6l4BAAAAuEgf/vCHzZe+9CX37vzdvn3bXL161b6+d++e/esddNDztre9zb0CAAAAcNEIegAAAABgR6qCHtr0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADotEsS9MRmPDg2x8cDM3NDzl9sZuOBGQzG8gooF88mZnByYgZb2FlnE9nnJrO1+1ws051s6TvlW+3yn5xc5PEGAACw1J2gJx6b46Mjczwuyd7FC7NYuNcF8Wwgnz02oY/Gs7EEKhqs1Exjn8GMTRznZxjPZmY2k+Vw74Gghewnsu/MJpMNA2SZj+yPs/HE3Fo7o8XK/roRmdc2ZwcAALCJDpX0RCbqSdZtcmKOjuuXpsTjY3MymNlAZLEIfCrWQKVJSuYRj/XO+SAYSAFVouFNM4rkhQTyNyYb7EC6P+rfqG+u6/ycWI+RoyMz2GTeAAAAB6Q7QY9k7KanczPt28jHnBwNjIs/SsRmfHxkTiYS7vTks/NT+WwmZ+hEo1NzdnYmaW6GMmvTG5q5fZ+keTLQDOdu2OlIwi9gE5EZDvv2b093r5Y0CFdR/3pmn4zNLXtgRCbKRkI12ap3oRLOXJq40syFmQTH59Ok+kAFAADYWMfa9ESmPz11gUhcWdqjmbck3pma+enUBOIdYLtmA1vCUivZxjWxfCQplalOJ2a10EZLHfVvXwKo7M6t1dj0b8/02uzzWvUuLdUsS/7Yk7/B8YVEfU8AALBjnezIQEtn5vPTpIpQiUiDnfncnE77mbvgwG5FUZRLqcLwcHLTivzwQHGQBhP6t9+XsCfDD9eZZQIP3+ZnEQxqljcQouE8LeUsTXNf2tk309D4QprngjIAAIDtu3Lnzp377nUjd+/eNdeuXXPvLopWUTuxJTZbpVXYbDW1bGPshZlpVRwzNFMJlHw2czEbmMHEmOF0arRmnaUZSm0rpMPT4Msvq2YEZVo7oaeZSsm8une4PGaDIzOYRWYkAfjavH88MScnYxNHIwnYhxX7S2wmJye2PVk0mmeCiuXwZsL77GRwy1yfBpbDL2fwc0KOmZNZ38zlOAIAANiW27dvm6tXr9rX9+7ds3+9gw96tGeqquoxWr1mObpnev0awUWvb4YjyZDZwKV5RNUbzs3USMauTtCjVZ5sVSZtF1RdOoXu0U4FTiQK6U/PzLoYwE+bD2QC0n2qEPRkgqbpzetp4K4Wt26YgV0O13YtR0uT3EtHq90NtC1OKACrCnrSccWADAAAYDMdDnqq6LN5tJH0QoIQyXhJRnCykKCnJ0FMNDSj4Wh9Ox7tuS2NeXR+E7PoDc00E5lol9YTyV/2s/OToKk3q1fSk9zpT15rsHRK1HO5+CCgPzVna6Iev69UB0j50pxsYOE/Hwo2mgRfXrrvFgOfTHAVHC4vbfVSSnoAAMAWVQU9HWzTkzwE9Fgbd0uGTHtVOx0tb13b9g/24Y9H5uj42N6tLq3tE/VNX9tEaHKz6Pn3VcNtLk973spnLEP606kZ9rQEapoLpnBJRNeT/UXbziRDSmjbGv2r+5gdECb7tg94ciTg0ONBP5/v2KA9LRWyu2w8lmCm+plC+iysXAkPAQ8AADhHHQp6lsGOPoHe9Ie2G+p8HJH07naqjaenQ8n+aTudE3NydGSOB+PKLq71QZFa6KMNx+uIRlNzejqtUV2tb0anskySCdxOVhSHJTLXk6jHTCqemxPL/qcxSzTS/bZMLPOwU5mRVs/MiG8lD82t/nxTkRnOk1LLfLfYq5JxevxRpQ0AAJy/gw96tHrZ4PjYdturwc5C2+NIxup0Wqi+FmnVNvdaRP2RmZ6emTPJtA37PbNIS38GZlyIfvQuta3G0xvKtMmw5hZm0bx5EC6BaJgEIrG2T0sGFUhAZItvJGioeLZOPLmRlPJIwF9sl6O9rs3lmNhWKc+SVtes0QObVmc70+doEfAAAIDzd+BtemZmoA8h1Ze284GhGQUzVfq8EAk6KjsySDpFGGvglOlUwFbLSSKeYEcD8UovbV5m2bLSnuGAJd+mRoODYtueqnE5tgMDk7QZs69nSVWyGoFOrTY98cQMKkqjlha2m2sbpKVdGlaLNFAjIAIAABvodkcGsVbb6a/0LpXnApBaAYcEPxKp9H0GzGYe42DAY/nODrRdjsx5uRxJRwq5QqOob6ZTAh6ELDsgyAUeLniRoas9oa3Qdj+uzU8h6NFqnJUdEca+bdtqT22WHjv9mVuW7asbnAEAAJTpdJfVtkc1966cBjIylQQm/Vptcnpm2Cg4aRJUASXS3s3cc3vMsrezJr2qWbsIemouQLZHQoIZAABwXjoc9JRUIdtY4a66ZAhnlQ1yfHfWyfN9KrN4Mg21eFAqLdnRaphJINIqcCgEPeu06bI6KNNd9ag3NuO6D14FAADY0CV9To/nn4/j3rYpjUkzopvjWTxYJ1tSIlHL6sM/67iQoMffhCiUVLX9DQAAAA1csuf05MVjfSipvNDeo7RLq4VkxCQzmFTlqUk/K5m48uRKhSSgmgbHL9Pqs3iStj+DccNlQvfEMzMZnCwDHmWfgVNoG7aPZNkHupzyMhrdTEp2oqG5qft7jef4AAAA7FKng56kZzUb8ZjptC+ZsdPkLvZsYE6Oq5/LU6TP5ylPbiLRWxlXSG46z7a10AdTTiQ4205hEg6NVp+U/eBIgoaku/TIjPR5Nr5dmQ0ojlzws3+hQ/Lg0SQwK5YsaVfZ9pjTwMc+MJjQBwAAnL9uBj2SSRwPfMCjXU0v2+fowxGn2o3uQp/Lc2wGkgm7yGxYL/vwIFwqsQ1mNNjR0p0k2Inc82y0+2btxjn7bJt0evdMqouOf5LlkWDMlpxGpl9SlS6pMqfDtVTz5DBKrgAAQKd0q02PBjuTsZnYPqRFT0t4JOBZzYfZam/2Yab2nT6/p29GQ+1kYDmxPvg0nVel5XOA0q6u1+hJhjZ9ppD2nBUoBUIHyT46mUzsc2yy+X4Ndm7K/pfZ/fK0NGhywwVHWRoc9c1QH3Ca/ewO2/TEs4n8hkxJqXbFfjN8nGVpidCNXNVSDZSGZnh9XZfzAAAA63W3IwMNcmYSuEjuK9+7mgQfEuysffq7zUhK8FMMbLRra8mEDs3AlRZtHx0aXEJpl9Sey/RrsOOGrKf7rAQd2TZgsq+uPLR0B0FPMGiZ3lx/nOUEll9s3GscAAC49C5Jl9Xh0pp6tKRmZjNiSfBU50GQQAva/mVizPCmlsxsGPTaUqOFuT4N9Iy2o5KepGe5JNixVfDc8DaSEqOZWfRv1lpGAACAKt3uspqqYQAAAMCl1+0uqwl4AAAAAFTo/HN6AAAAAFxuBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAndaxoCc248GROR6MzSx2g4rimRkcH5tx2XgAANaJY/mfC8m5YX0D2NCVO3fu3HevG7l79665du2ae7cvJKA5Gsi/fTM9m8q/q+LxsTmZLORV+TRFs8EWg6RoZE6ndb4Vl00sAflCd81Uz/T6kYncuzJJRkCmWzehp5kH/SsfWP8RzWjo3wbz30ON15H/3bXWEcq0Xu8r9m3/89cauZJMz8whn9KbbaPYTE5O5HoYye+en+Pv7s76BrBbt2/fNlevXrWv7927Z/963Srpmc3sSdH0+6XBTDQ6dSdMLfEZJ5m/tRY2M9ofjcwok/o6XC4W+eF9GaLD+5lhyXCdSS5PC0igMxmcmKOjI3NyMjCDQTadmBMZfnQyqdhP5fOSCTmRNKm1M0um5UYy/Y0aH4gnN+y0JzeqlmHfNV1H8gld9/qZgT2joJUN1vtKkuPAHgsnZjCZHfC+uG8abqP4lqtFEcvllmMDwGHpVNDjT8J9CXqq9KdJCU+v0Z3DyEQaTGVScmeslxumqWcnl2AoN5w7xsiLZwPJbAzMOK2LqXdb3f4i+0+9/UWn17+SCblFVjCMdXQxdrDeY5nXWI6bIw1+DnNbaonuZDCRcGMfNNxG0XWZXs9TkRkN7QcB4GB0J+iJx5J51Bd6Ete2Pcfm+LgsjZMLjmvfE5pm9QZvLJPPbGDlU1INY5EbpsmW5ujFOTfcVSkCRDw5saUIdp+QQGc6n5uzs7mZz6dmKkG5vp+fnZkz+Tvt2zC6VH84sgFSPN6XjNT+YR1djNbrvT+V40H2/1ySY2LqbzZp8JMcQwclnpgbuRsdF6/ZNorMcKrnqbkZ2u0AAIejM0GPBiQabPSGQwl7dIBWSStPKjTcp1ULMxlLYJVJM21zYS++2eGakdXhs8wwTROqtiGhJTy+kZhk7jTQ0ZKdIC0xHGqVyQp699VOoMG1HYIi1tHF2Op6j0xkj5czMx8lR4QtLT3QEp+9wbEB4JLoSNAzMxPbOYGcv23mMTKj0+JdwmYp21CyN9S773MzPz01p8GU3CmznRQEx2vSu5Qyn2Horj2lQJdHLPuqy1nI/mLvXCfvGijuL5G5nuRaJNNyMbkWbQztUx3p9O59IxWfK1+O811HG/0+3b728+5tXf47W39vQ7W+bzfrPRrK+dQHPuMba9ujtFkv6WckXajMcrRbknW/Y5fHhv9u97amTX7vJp8F0G2dCHrisauuFlTzhBvPbJWD/KRaTW5gM6mz2cRMco3Ms2lsYi0hiseBcT7J5+WCMptM5HWmA4XZwBwdnZiTI7rRvhTShsBarWTYPOBJ95d8w+PIl3Dqfnpu+5EEcGknDEljaE32fXAhYttQPTe9bZwux1hwmbXHpqQBu63FFE/c9Pq5pCenRL3l2P062vT3yXvbYN//huR1dQ0u+c6JtnFx62XN984GyfdVdo7h17Nfrpxm36d2td7T+coyjf2NhJym20M13adDZB6+4wV3nGp1Vvv+xJ/7l9te0zbW8/r9yf0OGVn8eP1tVPiO1LrvTl6v/s6swLrX13LtrFwkq8G2zuzf4W26/C2UIgLd0oGgZ1nKs8JnEIMXxLx4NrYn3POuI768s6ZtgzjBdl18y2c4fAPiZpb7i1zkcw2Pz7uxvmYMTlzbBK12pN8vqayank4vGZGB28f99HZyveEgmaLqQ08/H+ptscly6Dj9u4t1tOHvW0hGTDJoM+0KOLf8SWauNJOr32mr1Ar5jP2su2sf+l4db8m4slVQvo82/76En8+213tm+RYLt8xem+3RZF8qp9sruYEVmVGrti9t13OWzkP3p8xvd2OSDlSKgYROo3+3sI3a7svpuheuQxf9qF6bdXnLq4c33NbR0NxMSwlX2zHFEzcsGpmbNFwCukWf09Mm/cRP/MT9s7OzC0/zYU+fM5Sm/jQ7fnpfzuMyvH9/mvnMaprfH/ZCn6+b3Pf0p4Fx69JUvrt3vyefnQfHk7qUpn23r0ajltt7en8URfej0P4yH92XS/Sa/X0un0+WIRrNA+PzaT6KgsubDg9+1/z+fJ4fVvW7y+flj19ZVvnNzT6raXU56q2jzPLWPKY3/X2aVrZHuqySAsuRfqeJ7o+Kv1N+e3j8uu1fPr7d97m0o/Vetm7T+TTYHs32peW2y14zlvOIwteSGuuh/XrO70/Vvz1wrau1jcK/e7N9ebnP2e8u3XeSVFzuNts6u7y5ZU2/K7TuSSTSISSNT9544w2birHLYZf0aHUyW8qj3UaH2sr4u1czM66qOxbPzMzeRvLTB+h3BXp5S5KrajMbBMYtU/jOXN+MtM2PnLnlZItOi/WGdKLXa7m9+2aoPbuF9pdzbJC88HVG9a5q8iojSu6yerFW7dQXkRndXK3St6ymVL7ccdwz0/nqZxsth9rFOtrC75MM2+pd5cwd6ZWSDDnX+POJPiRy9YZ0ZIcnH89W/1q234hnt/LzVGn1S/n89cxMW3+fs6N9M5LjKKHVi93Lltuj8b5UYEtQ3HWm9YM7N13PKe0RMvTbl8ulVa1z239b26jpvpzZ50a2Uxc7dEk+q+e7oNbHnqwfN89sm7DZJClNjkY36Z0O6KCDDnr0pG1DnuHUBPsHEFrMrRZyxsudaLO0PY7+DV7slrTdjha75x86qsl1MyxpdZwkmWm4RzhgmyIzdM/O2HVj/Z7PAdZop5FWl+oPSzISy5sNizTnWlBybDZZjsT219E2fl/Uv76SYVNppl4y5NkziD0XKclglj8uZRngZDOa0XUXMMerVdzKfssm35c4v32z7fZovi9laMDjopXWAY/YfD0nopHP7K/y18TiPrWtbdR4X/bbS66t2Tg7p+T43+jY68s1245zwaMPOENBG4BOOOigRxuC93pDM/V3kEL8yXIxc3eTVvkTfHoxqNLTk2jDtO4WIbAtur/p3y03Gi9a3kGVDMOJNh7WOvyhL4zNLX/gyTKljYwLyd/djuN8NszT4yik/nJkbHUdbef39Zo9KXlZKrGmxDCY0Uzv6Bfbbyx/S3F9b/R93g72zTRI0McEuN/Udnu02peUtmFxM4xGGvA025ZZW1nPonJ/knHJ2EzpmLeFbbSrfXnV5seef1C5BjxHdsJwiRGAbjjs6m3RyEynrrvoUnISt2c1ff6OHVDgi739dNUWvheeJsmfdXHJRXpdT8hOt5u9wu/H2240XqTVZ6Zm5DJ4+pR5bUysPU35BsWrXFeygdRem+XY1ToK/zZN2yUZXXcuiyK/QzWxvKOfq+KWVjMqngs3/T5v++s9zTBHcmwlrzLC20JTWJt9SQ7lse9goy/rdZPs8rbW8ybO6/zhbes3h7ezpmqyzbI3TUtLjAB0wcH33lanEMXftQwW2euJUf/KNLnrfAmtSqdPo26U2tZ1QOcs76BvWG++Qt0nrJeVOmRV3oWNJMMwnZsz2cf17rYdL9NrD02hrl6TB0sGjo9sanOsNFwO1ewp9PXs7Pftgj/fZaq4+apCVVWjNrXV9Z6259B1v1qlqtX2aLMv+dIC+UWDqq7A98XCXfPS0rG8XRwbu9b+2MuUFqktlkIC2D8HH/TU4i/wgbvrsQzTrF9Z9ZkVcsHQ9jmNks84ApngeqUh8bZUNkjOlDbJvln9/TXvwkZJ4+r5mW9gnc0wLb9Pv0sfHlyZkknbkc+XL0fB1hrWn+PvS2W+M9QZQcayjUixJKR4R39mJrYRfqEDA2sb3+dsbb3HZnIjVMKype0h42rvS/L90zMX+MRJ98pV66jc9tZzads4cX7bqI7Mb666CSPjVsduvq3jyY2ke3Ft32M3dGzGNw4gcAXQyuUIetILfPEkLhd8222bH7+edogwHo+bpbLnCOESylSnkAzSjZ1UfaxukJwG+PL9pZ0/qdkkfd7IakY4ZPm9Wen3ndtd1PBy5G2n0bY6/9+X3Yblz9vR85u/ix0qCfHzsBlsWQd2LZRU79nG9yW2sd714Z/+WTiybGlJS2K726POvqS0apyrar1B4LOt9VweNPngVkjEsLttVF+dDiTSDgsKNtrWaW97kRnJ79U2wnZuel4+rwMZwLm6JEGPnt+TW0K5k7hcWJKYZ3n3fR2t3naqXUw3SZVVWmIzHgyWD6JD50XDm8u7x9qA1j1EMCSWi/kg90RyfWq57C8SrVTuL36fDmUGJGPrv1+r7YTaK9jud31AFsgIaylVaJmXDcsz0uMruYsa/K1yLOpvaqrRchRVraMmdvj7SqXbUBvdh/afpEpWkr/NloRk+HnIsk2SPvvlp5Scq7bxfV6b9R5r+wxZTq1mpg+xtJ/T0pgz1wNXRsvtsdG+pLJdK2vgI8dP+c8rKUXZ1noOZtz1szJP+zrJ6Jfa1rFRQ64DiUApS7Yr8BWtjz0Z5s5vy+6pJXB12y/bjTWA7riiD+txrxu5e/euuXbtmnt38eLxsTmZLMIXQUsvmlrM7d6K9Z/JkIvIsT4VWntvq7ieB2mdcLlwhr5nNlg+LbrWcqAjNHi5sXwCuaPVMCzN5CWv9Kps5u6ZG032Fz+t9ig1L2aOYu3tyFcPSvjvzjX+jZLG3cWvSZdDxqePyJJ93Pd2tfKdLojyc47kc75qStqdfH9qznI/SDImR0kmrey3Nl6OgrJ1lF3P5TLVmnb0+3S+SUcome9KLT9vpetgId/p9x999knoeS+JeCJBRJqhDH1H1ubf52223oV89/SmLGvZ97TYHs32pYptl24zsbLNs6VUcj2SL+ppxa1+tpvrtus5s0wSDCQ3+CQwtB/OfjawzAHhbVT2uyvWh1e1L2fXmYhk+e1SL2RbyUJHo5Hpjcfh+W+0rZfn1kRm+6yMA3AIbt++ba5evWpf37t3z/71Lk1Jj5zB5GToXloSiDSs2rYLvgQKl01kG0zPp6Ncxi3tdci91wzPaLiswpJWBamhskGy3pXO9FSl8r0daYZMGweHM8HpcsR6x9olmzuRzJj+rmKu18/LfyzzOZu/kN85rVWNKK/xchRsrdH2jn5fNc08zpfbMP3OZP+x37kmAEmf2aOv13ZgsPn3ea3Wu2zTqD+y33GWWddBLbbHpvtSSr97WZRqM+RLctzfdNXgZElstUJZf3lbWM+6DJLJj+QT2c/a84n8lmBQUnCuHRqky5tI1otsq1iCNht0VVwnm25r2SbJJpHtutI9dWb7UM0N6JxulvSYgTkuKw5PLfQmnhUMPKJRvlqaK+kxw7k59Re0utxdrNI7YJrZ1Au6e4vLKVfCItJSn6La+8vyrmXpvufkv7t4g6Bc7nO192FZ/tzHNt/z2y2Hqr+O6tv+76sjuw7O4zs3+75drPcyzbZH+32pmfR71nxH/fUcLm1Zfr7+cZ04z22UIcubLnGzBRYXc+wB2B9VJT2dDXqyReWt9Kd2PpvOppTMP1/tAdgBX22E/a0c6+hisN63rEYVs6bYRgAOzOULerZ0btZicl8atHW9nulzFwo75zNCoTYASLCOLgbrfbt2EPSwjQAcmEsR9PhibYqzAQCXzy6CHgA4LJekIwOtr0zAAwAAACDvEvXeBgAAAOAy6lD1NgAAAACXFc/pAQAAAHBpEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnXY5gp54bAbHx+b4eGBmbtB2xWY2HpjBYGDGsRsEAEBGPBuYk6MTM9nadWJmBicn5mQwkatQSGwmAxl/sqtrHwAcjit37ty57143cvfuXXPt2jX3bs9J0HN8MjEL0zfTs6n8u22xGR/LhWxhTH96Zqbb/wJ0XTwzE9mBrk+HJnKDguK4JHPjRFH55+Wzs4XspE6v19fJW5Ll8AtS9Z1lMr8jWlmIzLyz2nxPhVi/ZN08626Xgng2k/ON1zO9fs1lb/l9K3Lbumf68v1NXPjy5/j9QZahbKaZ/SmobDu3/VxbEvQcDTT8iMxoPjfDjWcuQc+RBDTRyMznoXUuQY8EReN43bUvWcerxyIAHJbbt2+bq1ev2tf37t2zfz2qtwEXzN79PRmY8SzOZDRD3F3dinQjcAs5nujd5SNzJOO1NNKnkxMdJhmmylxf2MzePfap7C5ziXiS+eyNlbve+Xlnkv4GSSeSaWyxyBmaEZT5yDwnFbe/628XTwKNSbKcJ5n1PNDfoxlTN1WZ5t8XIEHHwG7X7LY+kfUm79du6D1Y/hVJpt1u/xtl+1m746L95zbQn7qbYrEZl/6eCxDfMjfk9x41PZYB4IBQ0rMVlPSguXg2MZPJOBN0rNk/bbAwtpmSsjuyvf5NMy3cPp4NjiTDK5/p901fUk+OhNlsJsl/cfPjws8z0eyu9brPZscnvzNU8tP+WI41Yz+OTTSam3lgoRtvF8vfUU/eRVFmXUsgMpv1SufR7vtC3F1/+zoy/dFQ5qHbejnv8vPTPix/QFoyIspKM1oeF60/VyKW9ZQpSK2wMBP5TT3dPj03qFRSSqc3Lm7MQjNfHhtlv8GWaCoZH5rC/8Z1xwUAHIKqkp7LFfT0hmZ+Ogqe+DdD0IP67B3xZa5f8yIu47Ims+gzaaVVWcK0qpLknKoziw0zOvnARfSn5qzOjp/5zkRF0LMyTy2JuGEGPmde9zuz0u9fXdett4tYrg+Zdi7T1liVm3xfSLoMK/uHrLeBlvTo6/C892H5V2WDOLEu6Gl4XLT+XImVY2Ib3LJJRGoDkl1YHvs+8N1W1TsAOH9Ub9smvevZ4Nqjd9l2c6nCwfK3g6O+GU01w1Eze7hoty9pCU8w/xJdTzO3cVzrFvWKdN56t7/GwsW3XNW0krvO1SLTH86XNxVkPTZdH7OJD/K0FKSg7XaRzH+S2dXMYr2AwWr7fUFaGpO86g+LGXhdb/5mz3K61F4s/6p4MrEBT1kJRqrlcdH6c5VkHeq6kKBhs5QPGiPZ78/OzgLJTafBUXD83Izs6tNANDT+zAU8KjJDuw1jM66q9wkAB+qwgx4JKJJqOuuSz9D5aj3rU3LHctVsIhmEkyNzvPaum95dTdoNpHemSxEYXSq9oZlKxuZMMjbD2jnMjF7PZWA3FemsNqPVoOzCyP5+a91ePDMTeyxIxnCDDHJvXSa4lB7b+rcvmbvAPFpuFz1fWP1hs7vjm+4HWXresi+0Wpp9kZcJcNPldfZi+Yviiblh9xXdVjV30rbHxdaOJ6cnAb0G9RslN6/zJjuP3X1q3sQAgENy2EHPYpJpbFuRJq4nooVkukLjA0mrqq3ymaY6vSFFMk2S+1i4O5ZBs4FtZHxydEx315eEbVvTIlcT+zvrWxMvb9ZHbaOfnrs7LHMbV+znymfMJVC6vkGwtfB3JJpmVv33+4xdQbvt4s8JzQO5tvtByDJwCf82Xb7r/pwlG315qtmP5c+LzeRGUiLXn66vJtf2uNj+8ZRnS/kbJ/fh2uQY0O1Q0TioZ8dHMmUdOq3+rXMTAwAOy2EHPXqnUS6Ka5O/U9jrm2FofCCFbi7G47HLNI1clYE19M6pnc/MjEsimuVdVy2F4iKD9doHKHnaAD3ZLUtKPupKM9o+Ax0iGVlXZWa1+lUD2apYDTPp/ljzNyO2QuaZzLWnN/gTknPV79LUPBPbRsPgVRYqze7vxfLnxZMbyX6Z9nRWT9vjYlvHU2+o146hua7rUds7nSQ9wDVLvg1TL7lWDa+vOVYiN11JFVYZ2nfLVT2fJX98tK3yCgD76rCDnigpTVmb/N1IOYcnd8XWp9UbmJoRSC4COr4eyZiNkmnLSnv0Tuawp8/BkAtTrUgKl5Uv4YjH2gVx0n2zTZJZWtsdse2ByyXZF7WKpm2Eru0xCu0HmpOgye27Ou/gkmQCrNqHj+Tk02WWz2v3wv4ZJ/1p04bWPjCIzJbyuFZaWiAnjJ4c4cXuojfpFnzbokBdxr1bfg0WkohHMur1dpS2x8VGx1OA7/HO7pYSQWavJ9UptCPLflocZ2sFZJZzo5TpIKJIq+fpXz3u7AAA6IZL0ZGBVhtIaObJvWxK6zjb/IFeiOyQetaW9kim8/TUnMoFvlEeDvBk/9beuaqesRHfylQF1RLLWDNVIzO9KUH3Fna86LrbfyW4CtWK0cBFBTsQKBOPl8s80GXWGWtm0h9TTSxcqUWmRGObZHlu2eDAZ1YzGVZZJ4P0Dv4u+N8mi9H2x13o8nvyPUkxXq1qbWvVOC6C2n4uK6pTC0GOBVnFCxdgRf2pma/p9S7Y/seNU8HxueQmrBLJMWJfLLSvBwDojEsQ9CxLaJTPfDXlP9cbNsi0WetLe4A6tDv0ld6X5lMzSjOn41wXwjk9n5FNMrORHBfxTIIKV+Kzcd5GMnlJbbNAz0/xxD0EtGE1OsmhLZdZks2x6fGclECcNGlpHUsGTv/KPBrHSxXS9kWyLsdGe9Cam3maqdVG/ste0yZ72DJ8f5Zfu0t2gVXDam1tj4uNjqdN2UDyxAzGcuz53u/W3fjSoMj27FZIfmWVjc8l39tb1XHQqxccAcCB6X7Qo9V6NLfTc5myNkX2cvGTa5Oo04FBQI22PUArkmEaSoYpzSSW9LoUyT64vMMsmR/J4M2nSYa2+LyVttJukQvL4LuJloOn2Q2DYps9ybRpF7xTdwxqtaRd5Umbi8zoZqDdRBoM6nq+tXlwGbTMpC5a35q/yOWXeft2PNr1cpOIp0zN42JF2881oMebrzIYjTRQ2UHvd1sR670CAOiMzgc92sbAxjyS4RpJkiGNAw8/j9odGKygtAe7tXwOS/2MigZCaQZzG5m7tFvkONPzk69SKseAzz1vRNvz3EyPw7Ylt9uSdp8tmWXbgD1AS6msONOBwAXIt99J7MPy26DbteMZ3bxuvyfXo1kymaUlU8VhVdocF6r559xy1UgLtx36o7m5qd0YBqbJJTv1lvgSTwC4hLod9KQlNHIx1VySK3FZTAb1u4fOlPL4rnlbSe9yU9qDHUjr4TeU7peSudo4N+QfbqiHTRLc+wdMVmWqm5NMu/+xkoHkaFquj6oet9KqbE27+t6p2NxKq0PKufEk0KOZL86Tc7FWCcsNW6ftcdHwc1pStbLcJck/t202Do9fSbsozqy1D0S6GgCgMzoc9GidaVfKk7bD8SUuCzMZuCo3a2xeyuNpe4bkCkJpDzorE9xrKcwt10h7o26qt8FnYiXjv8073csOHC62FCctsSkNArWEIXmV7aJ5L5ZfewuT5S9NbjIVGrYXcm3m1iS38L63t7Vpm5HHYsslRwBwQDoa9GjvO65RrLYLyEYrvn3NYmJOjtcEPtsq5XGi0YjSHuxEWqIie5jkk+pL27g1/FwpCe7d8aY3DGzME43SdiFbkXaMILPur3uOiefbvWy5R6q0Sl95Q/+0Cp5mYJNXW7cMXsbpusmJbyXbQqbqZ4vcLnz59Tkzxcb2heSrYMp+dLM4bI22x0XTz+XbzFUn334nea5PjbSN7hULqp9NtOOeDgHggnQw6Em6G01qBPTN9NTXzfYiMzp1PdisDXy09yi5OGxcyuPJBVQiLvuwuHSGsRnbboS30IMWOkwzpeF9RNtE3HBB9EqX0LOJGZR+TvZ/X3VmixnayJesSs5Jv7d+YLKOzM/23OaPWS09rTtnXwVsG9X4siJz3WVi4/GNlXZRurzLVbyFNSwB30noOSuZDgfSGz6p2Exu+M4kZLrcKtuT5W+t5XHR+nMHLE5+s6rs2tzfCImqengDgMPTraBHuwE99gFPzwx995wrJBiaD5MTug18BuE2PpF2JXpqzmreVayjPzqVgGfZNakGaBO9yEwG4Tu0gBOPJcMvGUZ9art/fo19Lzu83X31TvhKEBCbWehztqtqlxHeVo9Zqezd8SaBSYFkPvMPVEzaN7jsaOOHqvoqYNvu/CAa+o4VYjPW9erWc7KOk++KRpkewTbhqycFgtTls23kPGjXlS6H/JXX9vwm57PQAz/3ZfnbandctP/cQcgeO7ZtkP7117ns8bnKd3ixvZsVALAfOhP06N25YzmpJ4/k0YDntLp0Ri5op2ngMzMTuSgc2wcg2rHnppe2yAaqLLsljrUbdg2UJSW7ayTBtHZ9G2g3o20NQp9LcnXln9uQ7/1q63fKZSXYZT6bN36oaloCpevADtmWSM43ma603XpO17FW39pS5lnnq8KlLnozRwIf+1VaKqbLkQQZ+iDa9BktK/Zl+dtoeVy0/tyB0HZS7qVuyGRbJvuB3UeStwFyLUwi5Hw1SADogCt37ty57143cvfuXXPt2jX37uLoBWsy8MGOsM/2GKUZvbXk81q9bJKt8iIZxaFk1kYlM9HMRBJbJY2jF64KkdIH3jW+I6oXJbkCc4nBei4Ds1iYhd3/ZL+pueNo97dL9T/XJbPBkS0J1pKLbWXki5bredvrWB/gqaU2EtyseXJ/up8IbfjfxH4sf1Ntj4v2x1NdOvvlLP06aHmtCNFSHd2p+9PNayVsc14AcAFu375trl69al/fu3fP/vUOOuiJx8fmJI1WtK3MSIKVNU+1LmHrrsvJPplb9UXZZ5xW7eJiDmBrtE2JbRN0iMeqVlsbmNnBZkgPfflb8EHECq2e2by0MmhrgYoPyLa4bABwzqqCnoOu3hZp+5h+z/S055x5vq1MU5FcME7tU+qnazNDvV7fVkvLJbsMBDzAXouG5qat96qNun2JxoHQKljyp7rnrT126MvfRi8y/WLRkbYV3cOgQp81pCVQ0egmAQ+ATjr46m0A0MwOqhidB217spB8dL/9zZ0LdejLv8dslUQJrlqvV18Cqp2aHHJbJgCXXmertwFAWxtnFIHOSNo2NW3/BQD7hqAHAAAAQKd1tk0PAAAAAKxD0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE67FEHPbHBsjo+PzWDmBhyYeDYwx0dHGy2/ncehrgAA6AA9D58cnZhJ7AZsbGYGJyfmZDAx4VnGZjKQ8ScDmRIALrcrd+7cue9eN3L37l1z7do1926/zQZJwNCfnplp3w1sJDaz8cTMFu7tRnqmPxyZfuTe1iEXyiMbsPTN9Gwq/zYlF8ZjuejJ8vf6U3OaroRYLsIL0/hn9frNlh9hsexXi+Xa78l6jdatV/lMZX5JZlA6izbfV0qWwy9I1XeWyfyOaGUhMvPOavM9FWL9ktA8G62nkmUNku9a9wPimZlMFub6dLjZb839BjnnND1g6y6HfE/lz99wm8WzWeb81JNTz5r5yXKn5+me/O61K9zZdH3VlZ7LIzOaz81w46+Rc/uRnNujkZnPQ9tKgh4JisbxumtHsh+vHosAcFhu375trl69al/fu3fP/vUIemqJzfj4xEgeYAt6Zjg/NaOG15blb5jLb2hzYXIXR3m1DHyWw5roDefmtOkPQCqeDMyN8SycWYwkc3JTMifB1bt+e0WjuZkXclLtv6+c3x+t0gxXiXhiTk7GbnlWM3+5eQdEsv/elP234SJn+Ixg/pzQZj2tW9acNetJSwFuyMzi1jc3hGT6BzdkH1n5EZH81pu1zh31l6Pd/rieBCCTG2YiG2h1W5Qsk+xTgxvj4O+uDC62sL6aSveZpsdNkNsGmwY9/pjcyjIBwMUh6EkDhk2DHrlwyAWh54ZmLSSjMJCoqDecyneEppBpJieyHPmgp1GmqS7JFJ6Ffmg8NscnEyNLuVyGdXdqsxZyYZSFJejZjN/mUb9v+pJ6skVms5kkvyXKM3Y+WCi7I9vrS0atkMNr/X0V8vtts7vW6z6bHZ/8zlBpSvNl9mI5Dk8kQ13MkLdZT7PB+pshtkRJlRyX8WxiJpNshr3tb8sGIZJpHw1lHvoblvOuOgc2Xo6W+2O1ZUCqIgk2022hpThy/lxZpmwQLdOPdPqeTJ/5LeHfvdn6Kopl+TIFhBUWZiI7Wk+/L3ypyEhKnWxAHqxqsDw2yrZBuv/J+NAUfhuVHRcAcEgIetJMVE9rPNQgGbHT7IU1E/SUZALi8bE5sUFPeUCQLEdZ0FN32SrIFddeFrOZK1fdI62io+/lF7S6gemqZhD0bEar7EhOZjUDks1EhjIeLe/Gtv6+CtnAxCoLtIuyGVSrIuhZmWdSAjDwOeK635mVfv/qsbyL9SS/xmWsV3+nbd+RWYl6fCb503ZBT7reVvYPWW8SnCVfFfrdLZej5f5YZblfyXfP5btrzDS7v8xlf8h+pHydtF9fZZbLvkVu2SQStAHJLiz3aR9wNruJAQD7pCrouWS9t0lQoIHB2uQmP0f96ak5Pd0wBTKA8WxsBoPB8m603jnlYnahtCQhuAmi6+m2iePATrhoUCqX0fr7akjnraUENRYuvuWqj5Xcda4Wmb4E3OluLgdq0/Whd//1M5G9q5+3i/U0k2MvyTsPVzOR/kSjpRNTzXjWyVqX0VKQ5FV/WAxAdL2N3LDldKm2y9FyfyylpeV22TTTXS/gWf4e+Ywsd/Ej/akLWOKZuZVb2A3WVyVZDl2HEjRslvKBViT7/dnZWSC56TQ4Co6fuxtsGryFxp9lgvjIDO22j8140uhHA8BBuFRBj1ZVCJ30V1O9O3u7pXcbj81x4O6eliodD7LVUJqSi5pkxjQYqk7ZO/JVtpz5ubSieqV9MlExc9dOze+rkgbRsr/mc5UBM9tOQ79XM6ht9WyRZRs+A9uXzF2TebRcT7EEgu77pqESqd7QTCWDeyYZ3GG9HH45+WFJNlWrg9kXeZnATavs5Wy6HFvaH9PlCgWIa/Vkv3Av69hkfa0jC6JVzTZLbl7nTVaGXR01b2IAwCHhOT2NSOZeL5ah5G8ALwLjXPJVq7O02pmts57NVMUzMzjW6hULmd2kENzovJLhg5PjZVUfJTPRefXX5tD086vLt5pkOveJUlrl7ejEnBwdp/Xw0Va8vOkerW7DeOtFkNXfV0/P3R2WuWkPh/ZVCd2n9K8EStfbfp1Y+AOpaWbbf7/P2NXWbj1VlSop23ZoS7lbPV6t0t8Wmes+Fy8/Jnuotl2O7e6Per7Rv00DYtkH7KL7zxfE/hyWD4o2WV91aVua5sl9uDb5/fobKhoH9ex4Cdzd+2o6rf6tcxMDAA4LQU8j2gA1VCIyMBMX9dhgJDBeU6jBczSamul0mrbxiccDc6zPVJBpe/2hmWvbolx+pG9Gp2e27rpt3Ds5MUfaHXWSu7LzmtZtb6NtIoIlXXMzrJm3SzMPuizti54gtCF5EjhWl0S0D1Dy6n7fWmnGsSTjacVm4qrMrFYnaiBbBaphaZHfV/XGQBOt1lOmlGejdVtLw6BMctaBU1FrW9kfZdskWycTnMhyJjdfwjeMEr5Klu4a7jyYkn3uRijw3M36sp3YTIfmui6/tpM60efjNE2uOqSsh6Gey4fX1xwr8vvtdCVVM2Vo3y1X3b3QHx9tq7wCwL4i6GlEG9eG6l/P5aKTXDz1whcarylUwyVlS3eOzInklLR3Ne2a+nTq65Wv0m57T7U6in7tQj57cmSOy7rb3SGtMz/sSUZFlqd2sAW7vX2GbjbRUjvZ9pqb13YVhfr8ni/hiMcS6B4dLZNklgbrAs4W31efZOzdttd5B5ckEzjUjjkkZ5ous95MkN/pn3Gix0ezWMJndCNTmc/d0nrypTzLgPDiRWtLgJvZaH8sSEuNIi2RSM5ndj7uhtGJfV8MahztwGCkmf7kcxo42H3lKOkFrm1vZE3Xl+9pzn6TRG621L1WCi2b7KfFcbZUPbOeN0o+uArQ6nn6V48BOwAAuqE7QY9cgMPVBbJXyfD4NLmpKslFOVQHe3l51OoW4WnCkrY79oKu1/2eBlanEiCVTZ+hGbG01EcyIRPJHPhSn3Ojy5B0olBjieHEtzIlgmNtnyX7SH9k9FkwjfNnsu9qb1NHJ2VPZd/y9wVE1932l6AhVCtGgwhVVtUrKE464UiSLrPOWDOTw9olkUsLXU2iuu3HVtZTWsqzWdul+vxvk19X9ePOS439sZRs11s2uNH1XggK9MZQWhJSkPnd2nV0sq+oUHusc1hf0TApda9McizI1y/cCVtvZM3XtCcNXVeyvyA4PpfchFUiOUbsi4X2VQEAndGRoEcb5oeqCiQpqQ6jN8rC49PkJzwn2lXs8VHSdkev9toz1dlKdbb1fKmPrdZtS32O099cSb//+DiQwlXxsEWuLZfP1EWyD9ue9lzJQiivEeyIQ7b7KM0Ujsv34Rbf14hk8pL8faDnp7ZVvSSHtlxmSTbHplWekjv/J01aWvu2HTKPynhpC+sp7aGuVYP8w7HR/liQttOSdT022hPZ3MzT4EA7WVj2pjYpbHf7fBnb6YpsLw1Qbe9pyXLo9rPn/Sb7ynmwAZyc+7V0Xm9e6TKvu3GkQVGgBoF+rnJ8LrmgqvI48O2kAKBbOle9LWm02TRVZoOEZJh2EAREkikayXdr253pVC7PpUFIjSQXfTPSKnQ6P7nw17zBHO6ym4hn13TbL+/4SmZEMoxzV52x+NyUSpJhGsrn0+09C/e6tLXvq5B281tYhtZVvbRXsXSZJUmmTdub+VJQrVa1hcXO2Xw9+R7q9Oc2+rUbWGZSFxd9a77m/lguMqObgfYnaVCt2+HWMviUgPqGXd/yOc3Ua+mJBjtuOeYuWMrvKxe7vnQ/8lX1tE2nBiIb9963ExLwcykA0CEdC3q0tCSTSaqb5Gq6LuyxZKLt9t6mbRO0atjIZgZDwUeTtJxfzcxWf/OODLA9muFO79o2zCwunytSP6OyyfcFpd38xpmen3Tf17/bquql+/jN5cN9k5nvVKP1pMe6faE3U+yLvZFvN7NbTffHtBtyCVZsRwABaRApJ1I/y2VAXVKqJsHSTd/erOG+0mx9ye+U5aqTknO1LPJobm5qN4aBaXLJTr0lvsQTAC4hOjJoQgKabffelioNQDT5et7lD5jTVDfWwR5LS0Mks9Mkd5LWw2+o7fcFLXvS8t1XxxPXjXVFZra5TDsNyUBuNVNYpuZ6SjPW6fTnYbk+qnrcat3Vdxtt98dG6vXClnZIkO4r219f8eTGanXpkuQfMzAbh8evpG0XZ6pa+0CkmxEAOoOgpw5/t603zNSNzqeNe28DuiDN7Celm7dcI+2NuqneBp8Jl2N549iulC/V0tVwvgd7WlJSGgTWCxAuyrIjjF1un6Wtr69cW7A1yX217+1tbdrm9lpsueQIAA4IQU8dcvWz1z+5UIZ7xMlWf9D64uFp9k5pGyI6MrgwLatHpSUqDT/X9vvK5buvtjFPNErbY2xF2jGCzLq/7jkmnm/HIcdym1xfnfW09XVZ3zJoGKfrJie+lWwLmaq/vSK3Uo33x7Rq5GpHBd5qKVq9Epv0c5nSjW2vr3xbsOrk2+8kz/WpkXbQG0Z1IOd7t+tV9nQIAIeGoKcGrVetev4K2yHFdkE+1ac95w2SXojcEFTQ6o+SywqtK30IZlqVJc3YeZoZLPvcwDXmlsxMsUvo1t/XXjR0y+BKSOsHJuvI/LQzgRPXjkO+pX5vcD6DXFI9bQvrKc1cS3S1szOFBHwnoeesZBr624d0Ji+d5UM6S9u+NNZyfyxbftk+110wEI9vrLSb0nkuN8FyjsvOM2S/CARL2rNb6HPnv772QJxsM1XZVbcP3ne5HwPABSDoWUu7yNVcUk8uml25+jmV7Yg0VT8zQml3sBO9SE60XZMbiAqyP40lgyYZP/sQRdfey3bBbLvdFdFo2XA+Iw59Tt9Lrs5/7uZKDq3997WXvbvfJDApkIxs/oGKSfuGJGurvXWt3z+zfJWmNDjJ2XQ9ZapDbS3IC/DVkwLBlz4oOBk2MwO7rvQ3yF/3kE5tVzXd4nZutT9WLH809B1UxGas693NN9kGyTbTB43mfkKmo4LkIan+d7tl8cGXnOuKP/2819e5yh47tm2Q/h3UekCw78Bhp/sxAFwAgp519EF39hqgVdTskA7omaGtNrH5fbwuln7tlNb9d/uRfYiiBoySksLEyPRtF7ah9i++elbhc3ZIxedaf99m/B34Rg8jrUNWgl1m7WGw4UKnJVC6DuyQjI3Xk68SpMfEttfmki6TypVapPTBxpKRt1+vpWL6G5IgQ59fkz6jZSva7Y/Vyx+ZoW37mHzKzzfdBtoFdWCjR0MZrj1g5n63WxbdX/RzweDlPNfXOZN9MF1TsgL9vmmfY1T5u2auy3VZb+dQDRIAztOVO3fu3HevG7l79665du2ae3fRYjM+Ptm8HYqWfBQujrPBka0e0ZML66nvJzcgHh+bE1mA8ulmsozaVkYCjvnp8o7mIHleQzWtcuZeSpCxNsywdyj98ymS5dIHCW5607L0N+pFVTIX5WsHIb7aZKJuUO0yMFoN0e4L9YPxdt/XLf541hKDUAZa7e96is3kREsh5PheWwqbyeju9Ac02R+bLH92OzTZBsvf3fZz215fOtvlHP060MvNlnrc1FId3akD16/GtjkvALgAt2/fNlevXrWv7927Z/963Qt66gQFK1xQUTzR+wuAzHEZqIStBATpZ4uyF/wtBWtF2svc6WZBz0w+N84t/jLw2trFGjhv2qbEtgmql/HeL1oNa2BmB5shPfTlb6H0OpA8TLVtzc+crQUqPiDb4rIBwDmrCno6Vr1Nq22dmtPThmk6DAdK2ohVRmgvO1UBT0IbSi97B7LVZTQAy6W+BE/ZjJZcXE5DbWk2TC7g2YSuj3znBsnQvgR1BDw4WGkbkPJewvaWVr+SP/vY5XQth778bfQi0y+WHEX9vQwq9FlDWgIVjW4S8ADopI6U9Ig4NrYaQcuqCbYqhXx29dM639BwAIdpB1WMzoO2cVno/RTX3fKhOfTl32Pl16+afAmodtaxgzZ+AHBeLkH1NgBoZuOMItAZSZumvXyeHAA0QNADAAAAoNMuUZseAAAAAMgj6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKd1MOiZmcHxkTkejOXVOYnH5vj42KbBLHYDAeyDeDYzM007ODTj2cQMTk7kuHcDNjCbDMxgMjPrFjOW6U629J2XDftCFbl2yrxOTgbnd+0EgHPUvaBHL2gLYxYmMn03aLf0QjExi8XCptl4/YUK8OLYZcJsRqzBntP2cyXaLse+LH+pmWQKB5KBlKTnha1bJMs/m0w2PO5lPnLumI0n5tbaGS1kve9wnW1Ktu1k0GB96Prz+8IubxqxL6wn89rnXQsANtGxoCc2Y7lYqH6/Zy8GjZL9ZANycR8cJXfFesO5mQ97cg2amJPj8YYXPXRdrBmwoyN7V1UzYTadnJgjeT+p2nlivZt8ZKfLfe7opPpzJdoux74sfzU5PjO3wGfyXdu+gx0Nb5pRJC/isbmxyQ/QDL/+jfrmus7PiSe6bo7MYPsrZyfsfiHbdizBy9q4Qs+fdl84We4Lg2Rf2H6JOfsCAFx2V+7cuXPfvW7k7t275tq1a+7dnpAL7tEG5fwauJzaq1YNWqVNS3jkZfZzs4FWcZOhvb6ZTqemX3N2uESy+6lkbEb9vulJNmcimR2JvXWgGc3nZljcdyRgODlxAbX/XG9hZpOx8XnE/vTMTOsWcbZdjn1Z/kqxmUhmeqzz7U/NNJJgS99EIzOfD2UJt8iuj4V8zU1Z9nZzng0kMyurNBrpzRM/D/8b8utTM78n8lu2t642p1W7JpntKCvdTM/k/OferVreMNL9pT8ayrSyL8zYF3axL9jtszaQXJiFTBPLGon6PTmmq0X9oRlygQOwZ27fvm2uXr1qX9+7d8/+9ToU9MRmfHxiJjbe6De7kElObbZY1A569G6m3jVcBjw6C7lQuI+mgY9cNvoS+LS9+KGDMhn/fKZGZTJngQyZzwxpxm0uOZzguLoZubbLsS/Lv8bq/PIZ37N1uelsYLdVoYDQBwDFQCE8fJ+CHluyk1lPeg5MAt/qoKd8e8u5eODbqKwLnOphX1hOt02rxz8AXLxLEfTE42NzohFPnYtYkbuorQ969IIsAU8uoFmYwbFcjBY9M5yfJtUbdEq/PKLXH5rRcESpD5aZj7LMfRpUFDNEPtMTyiipdePz2i7Hvix/lTSTqxnEuWQQV5ZBFt8FXqU0M++OX0+rwFqSs1+/eFplNnkV+bshlpw3bk7zv89nqovnLj9c1vVUq846i9nEVh+LRjKfldvxPTnf1Fm+7VjuD305xw3N0ExcgFAVsCy3QzCzngmsNw3s2Bca7Avpeq/adgCw36qCnm606YnHZmAvSnKy3uQKWUHvaB7buubyPVp1TQKcpARHv3Mol5eFmZwkF1EVjU7NmWQK+3Ih0guT1l0/louWu/YVtGhPhIO0cLmfqH89nBmJrruMmQTYwVbMkpGpnYsp13Y59mX5w/SmhM/kavCUzeQqPW5Hdrl9CUVoCS3NCEvwlU3JqUXmezM/PJhuJt+jmdSbuXGFTK4swWSSnDWiKJtrXQ6357e0zcvAZnKTwcthy5RUuT03vaGs07mc6+R35Vd2Od9mRbZHP3S6TvchndSfUZtiXwjvCzKvJp1MZLn1BACHqgNBz0wCCndyl+uELYmpTNl65zVoY9vjI3uy1+/o6QXwtHABTe++Le/oWTr8VC6QQ23zoMGPXDSOJPjRkiE/nQzThrsnR8dJdQt0WGwWa3Okkem5/E4cZyfu6U1lIRnGUL5Dpk2mrhNUtF2OfVn+ANso3leLqigtioaS2cxkdvVGRc3jrmd/gBzja9eBTHUryUSXBoee3qkPfX98K1kuPYfkMspyPnHFyf1pfniSzvcOfSRRSz9XerFeGsjoZ5NXBZG57k+wssM1Pi2yL7i0ui/MBjckUBrLb20Y+GgpkAaGup7oSAHAgTr4oCfthUdyWj3tMlTvIq5JdU/ZWkVNe5lKarP1zXA6NSO5iiwC81zoBWkqF9CVZZAPS6B0au+EJrnBxWJZsqPTJLQRLxeTyyIfEJTIZfgiM5TgWdl9PrerxGZyI6kOFNkG4fU1X47Eviy/ymdYi5lcOdbscbg85rKZ3TSDXOOZKNH1pB3S8pgt50vEepURXOYOfoF27mDXh2SUNajQalE+uZhS5IcnyY3aW8vAOV+aUULWY409LcW+kE1uVIYGR7aUKq4IfOSDuS2TqW5oqwIGI0gA2H8HHfTMJChJ7uZlqrXJSfns7CyYmtZ8S+pJa9uduTnT0p1C1YK6yVZB0DrvU63yJhcdDZ7cdUPbBel3aAmSv2uHrlqWgkhuyVXxKdJMmXtZpBkOibojmUarS2rmznf3nDSzmdfMkLRdjn1Zfk+rMJ0sqyZFIzM/K97VX5iJPQ4LVX00syvTJoeczGcsmWXXVbKdV4ivdlX62z2/Dkqqb3mld/a1py190ZdAscn66I4o3dHqYl+oS69n9rdWBT5OEkT6GxJyfDa9iALAHjnooKcvVxEJF8xwwyodaaPUFZEZnfq2O3rSX63bXZ6Wd6z1rltKXvdzdeMkGJLvOJWLyXYuadhn/aG7q6wZf59BS2nGbVCdicrcLbYPBk333UxAUkPb5diX5beZMZcxVcmxGehYoVIk546zTE9ySca5/BkrvtrVzEwqqvjEk4ldB9WlVv7OvpxjtPg4I60O1aLUa//pwzSTV9UlH/WxLzSlvzW5Zq6rcpeMk2uWVp+7pAE4gO447Opt0cicni17TNtULjgJKlYfKEl6R/EkudjtQ7ey2CPR0Nz0O6zNrOVLPAazYlC8ZHvKGuhdV9nH+lqdUoNrrXKp+1ySSatd377tcuzJ8kc+w6wlqJpZHba/aWCr7Ljl0HPKzYrMXTRMMp/xODm+V0km2N6yl/WQfbJkQTy5kdzZ12edFIK9aCjrZTq6tKU8TbEvtNE30zNdV2vmq+vjTKvEbfv7AeD8daAjg1VachNK5yLWh5Ymd7vT+tNARpKRWWbM0hIPm2nLVMnp9ZaZt3hibrgMlE4zn0qGS4MF+czQBg9JCUw81sDDfmKtVssh9mL5NfjS+UgGdSv5Qbcc2uNi9exkOhv0JSVdRf7Ovs3AVswoqb6VqZZboA9+7Oapw3doYWzbxq1gX6gmx16o2vVKmriSWzmek2qA69P6B54CwP7ozsNJbbBRp7vW/PN0lH+mzrZKZbT70IlciLk7hrU0ILcvNACwL4zvajfbxiXtfrdf/hyq9JkpFdOUqrkcK/Zl+UtJhtQ+j0XvbG+rZ7M4fbhl7pwxc89SqfVdEijOXDsP9zm/vrTEq/BYmLzAOs/pDW1QeiHWroPluqvar5bP/yl5HlQrl3Rf6M/csmxf5bkBAC5A95/Tk6XF8fauX1ladiLgLWx3Qr3qNgVyMTo+Pq6VbANYCXxWh7t5AZ7kVJJqke69ZGF871bLNg/1erxKG3/nek2rqdZyBNT63Dks/7mKzNA9e8VmSnVh46RLX6Wdk6zPUK9p2N5Zy7ZbVT0A+h7PdOKKvW8PHMC+oDcRCp36hFI2TtZgJjRNMRHwADgknazelratKUl5epdN/+o4/evvnK3S4Mhn3hqxn2vzQVw6aS9OF5wpbrsc+7L8uxb50pTYjE9Ocj1cbVrIYhuNB2/YJMm3qSqd7qJKeWpKnnEjSoPbekHy3ujCviCBWtKXwsg+liEe30gCOADokE4GPaUCXU7bB4XqOMmh2cuDZNrsA0STnNuK3nBqTrW3tQYpeXApsI7vxUnzHtl6/PXujqfPDNn47njZcqyzL8t/TvpTl6l1N0rWNHxHwj/jRs/HbnfJ8w/jlKmqOgDYKwe9L8zMwAZqkRndHJqh7aFRArgbDR9gCgB77nIFPSJ5ON0y2TuKvX7aiPTcOjxIycVFA7BxsftfdFV4H1u2DZAc1EpvTWlX0bNBsIczbQPhq09qV+4prWojQfyRbcuQ12Y51Lku/76yjb0LnS7Y557Ieg6tngun7Vl0P3BVsC5SJBlrt4lXuziX/cg9qHZdBwB74+D2hQxZ9oHreCca3UzWt3YMoSVI9jcQ+ADojssV9EQjc+qK/fUBoZrm87PkwaNuEl8NbbUa3G7YeuAagE20Jxw3EB0Wm1s3ku6dT3yJowQL+t4HDNN5oB2Az4gI7eEs+3nbbbQrmdQud3M1WhbuzrMvyUy1XI7zXv59IwGfHrNHklG0Dx3Wu+NatWjqgjqbiUy68V4+g2gP6DlG/0Z9sw+FJ8u2LhqM+X1B/vr9SJazrDezvXGo+4KTPHg0CcyKHRJoD4129WvgI9uEXtoAdEH3gh65uFSenl27Hr2brCkf22jpj/49v/YIPV/vB5eM7KeutNFniOwzQrSnp5JMqX9mRzJ++Xn7ad2nbYYrv+PqeFVeetJ8ORLns/z7QrvltsGdpOQhmHIesb93bobyg7Rb4ezzTNLpJcNouwJOVtGFif3NnDUPozw/LjjO7QvyV97pM5y0w5n93BM6sC/4YGygx54cdyU9sCW90elwrY0gAakLkADgUHWny2p7xzAppu/1JGgZLZ8jUodeCDQDllR3k4vWqbtb57muRGVk2j6hNpmpzra0S2y5CsYajLm3uAxkm2cyEM1LFrOfl30n+HFf5UwymKXd5rZdjvNY/m3w54WqdVBCzgkTfdaKy4x7msG9qQ/ALFtm+WGzyQ2XIc7SDLFW/RvmA0N3bqnb/a/vzrlJF/tJl+HJM5L2r8rYcl/YbQn75d4X4tlEfsN4GbhoadpNH3iW0xKhGzZA8jRQGpqhts3a5eYCgBaquqzuUNAj5OR8LCfn5J5mS9q+J1PdLbXLoAfYCZfJk4zZ9p57c2haZnS1LZTrhSvhMnqNnvavGV7JaGbb64W2xc6DHrcOtvrMm0N0OfeFYNAyvSnTN9kTAssvuKYB2DeXJ+hxtKpEm8BHS4hK71y5i1FvODenrm1CXdt++ClQS8MMVDdtcHdf1t/JxJjhTb0bv+H6syUFC3N9Ggg6dpDRzXGZdnOp9wN1efcFX9KnwY6tgueGt5GUGMk1tn/zku9PAPbRpQt6dsX2WiUXPE7zOAhaZVOi/562XXODLiU5brXtwqWtiqNVrBaL6ps6l8Vl3xcAoOMIegAAAAB0WlXQc+me0wMAAADgciHoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB02pU7d+7cd68buXv3rrl27Zp7dzE++tGPulcAAAAALtKHP/xh86Uvfcm9O3+3b982V69eta/v3btn/3oHHfS87W1vc68AAAAAXDSCHgAAAADYkaqghzY9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6rZtBTzwzg8HADMYzE7tBdcRj+cxgbGZNPlQUj83x0ZE5Hm8yk2bi8bE5ku8czNyAgHg2MMdVEwA7MBscyb55UrlvAgAA7NqVO3fu3HevG7l79665du2ae7dfNKPlM1m94dycjqLkzRrJ53pmOD81NT+ySoOek4mRmdT+3k1p0HMyWZj+9MxM+25gjgSBxwMzW8j66E/NaTpRLMHQwsjgZnp90z+fn9ZxhfXf68l6rbli41i2p/+kfK7WBmnxffI9leG7fL58DrLfHcl+Z/pmejaVf7cstw705/R1cdaKJyfmpPVNiciM5nMzrLmZ9AbMRI7N69NhxXoqI+veLqas47IPF9ZBG3XX20612p/LnPd+HtB4fn5b11GxPwDAJXf79m1z9epV+/revXv2r9e9oGc2MEca8WjGXLJbqxl9RzIjyTi54LtBK0GPBDADybCE9PpDM+otzHgyKwQNcvFOZhwIDGTe01HuYjcbHJuq/Fc0Oi0JZJbWBz3KZ0Cz62M5rIkmgSRCZB8Z3JB9LbThI9mON2U7lqxfLcW8Idts5aNVn2v7fev3j2g0N/OSCCANLiIJeoYbhDyFTGs8GZgbZaW4+l03JcAqWX1qGfQ0yzzGNldaP+jR0tUbci6KWwV9sZmcnCTnhmhk5vOSoMmf7zZQfd7YsVb7c5mL2c9XNZ9f9kbdWlX7AwBccpco6PEXGx+4xGZ8fGI0bikGPkmgoAUyy1KdlaCnIkNhM/59+T6bqclamOSGZU/zaqmFHbia+Vl3sauTIakX9AhXCrVI148OW3NHMmsxMSeysAQ9m8hkZiXbEknQ3e/LjjKTIFyS3xbhbZnNTEkmbjSUfWkhn1tWyVz93AbfF8v2PhnbaaKS6KDXl4xkMDO4PuNXmxy7Z5mF88dM8lv6sjfrOtDfk/6ayiDDBz11jq0lvx7XBz3xbGImk2w12RZBT/bcUyfokWmmw8wJp4aFBI+6XzRbD9vUZn8uc1H7eUCL+c0GyXWqShJ0i8LxAABYuiRBzzLAyV/YwoFPraBnRTGocoOzSqq3JfMuC3pW51c7kBGV0/oSLV+FRd/LEjS6geq5DBZBzyaSzNlM9sWbw2UpY0LvVPv2LxUB8komuOpz7b8vzby1uLPsA4tIvreskGchwcFYcrdRfyTTVGTYiyU9komVHO3q8mQzmzVKoOpnqNX6oEdLdvSmgKeLnORTmwY9hYCxTtDTIiPs96dm62F72u3PZS5mPw/a9vwsv080rF4JAJdMVdDTmY4M9KJm75TJRS9/AZeLxKlc5CRPtcjePW1Mg6ckI9KfJgGK3nlbSendukVueMoPc2/Xk+8dHJvj4/Lkq+BpVbl0uPud8WxsO3Vwk8jqaBnwYGuu3zyTDHkg0y5D+kNf/XFhFrmdRO9YJ6/6w2JGKvu55XReu+8TMqD+fpohmb4b9pZ73wzlYNTSmFC67hcouh4cn6bC3XK9i7/6W4TOx42Ilwfi+fFtUuQYG0016GoXScSTiT3PlJUSdEP7/bnMue/nZbY9PzGTc7hdDf0hAQ8AtNSJoCe9Y9gbmnnwlmXfTDXw0ZcaBNS8iGb5oEpLOexX6F3dk5PVNNDqY3Ldm+THJ985MwP/PqmHUY9k4LR6XGlyk9kL+sqwEA2ktKe6dSm5a77e9i/y3bWmHUnUM0mZh6zT7EaUnF+y22ogYF/kZTL8Wp1nqeX3ZfV6gYxkGd3Hk/2mP21SurENUa5K6bmT8890Pjdncy3dapkzzQaMDaurHZTW+7MKnW/Oez9Xa857jedXQvaJiV0Fch27iCI5AOiIAw96tNqCC3j0gnDq7+aFyHjNjAyLJUHrafUx+x396bJal7yeSwZnJU2H9uLak+/JDk++U5fBva9dPUxLqs7M2Vl5mrvMkVZTSYdX/kgJijTTsTatC56Elp4dSSB3VN0hA5qSTFwmz6vbw9KSj+RVQWSu+1yiBL3NN0X++1Qs82lqeUe6+XG2uVh/uhUVf0yA3sjI3pioTjfW7t+2HclGpTOxmdy4qIDxfLXen9PzzYmZtDrfbGc/r1qOVvOrMJsk+0Rk2zwBANo66KDHPlfHXTt7cjXQDFe4xMIlmbbftC1K2oPb6l02rX6yktILai83POWHubcXSjKm2eBpmeam7k3mNPMi4dGyITla02DTvuiZXrqTNMvMaxXK2tmu4Pfl1fpOpe3F7Mz0WOlJZi1wDGbSxO0v2vA/NN6nuplbnU8SmGgpSZ0jLE6qmtZMuxZPXGB1IQHjeWq/Py/PN7GZ3WqwTba5n4s6y9FkfqUypTz19mkAQJmDDnqikVYj0QzWMLmorUty5Wx22dBqYEl1Na02kw2qtAG2ZvJWqom5Lqy1kXZ2+MalIPa7xu7CvT/0jvRQVo52ElG/9AphM9lXki3c5q5u1LhuV/X3LVxGPx6f2IffpulEq2sGdmjtLloCZi1R1XmtLU30s7DBUnkKVkfKfmYix9rJUdKJgLancd+/Tq5kdG2al3RusiVaXTaJeKjC5IT2Zz3fjPSmkZxvbtYOAra8n4uq5WgzvzK+lKe8RAwAUNeBV2+LzEirgfhrY1nJxTypcraJxSKTydJkr0SxvcDl7wi7HJo2Zs0Mr67xsDATybRlL5DaG1uW3jWf6J3sjaOnbZNM5ump7RWPkGcDkokfnLiqYZJxz9/V1U4xkle9stvUTVV+3xqyMFo17OhkkmTIcqK0LUZCMvGZap7Z5INkvXkRGu9TKAaIb2VuKoy1i2PNgI7sM3oO74b4MlPeulrborq0LJQKp5hztMn+LPuq7hd1zzc7288bLoeqnF9AWsoj17mWnWIAAJY60ZHB7gTa02RzYJFksjTDn02ZNj3Z4fP51JZIlQVf+pDUXI9VhQm1hyMdtHA9O23FbJDrBW6ZLjJDdPnYro41Y6Y5IS0tqVlS0Vbd7wuWhMi0Ix/VxONcF82lfJXOQlru4vmqoMUUpA//TY8XmU6ykbanQlfiU5Wp9Hfi94N2tewy5ZtUa9MMdfamTI20V6thB859P3e2Nb/4ltuP6bENALbiUgU9vcbVfyQjMR6YceD6lGTG8qU5NqXBQrHL6p6tIhfWk+uaBkXLtPLcEgmwRvaKPdtqaU+uF7hMwnlI7vz6TLpWldHgeDVjpkFB8mqx0t9uE3W/r4LeLZ9mSl9mk5YNyjcTSUZwebzMzVwylvNp0pFJ8Xk5+yxtx6PPdGkd8Qi7LZelY3XSJl+3mW3tz2X2cD9vPL+ZmbjzvAb2AIDNXY6gRzPy7mUjsQQYE7n4zJYZKL1D6gMY7UhhpZenki6rfWrUVXXB1kt7JDOwckfSpvodGaCt5GGKSR3/SDaFZEQlR7TJDd201yjJUa5uvu1+3/K5J9lA/2JpIJQGDjUyqfrcrlC1r3CSY679oRtkgzN7Puib0c3rMmB5k8SmZDIrrUbr3ocUS8jWpX1XvT+X2e/9vPb8tDTOvtCSTPsCALChDgY9cjHRC0aaecg0Ym16oY9cNbP0AqTzkz9atUZmFWyLUNJltU8bNfbfUWkPzpu2M9Cn+8tLW+1G7wBX7RfL589UPXQzrbYlE+fn1vT7aogk8Hcv94pWd7MvyjKVy/Z19jzRIG33iIvNraTBhpDjWbbPyk0SX1qlPUgWhx20TffnMgewn9ecn+5vVro/AwA21cmSHq2SlmYSMvXlm7cFjczIRhma6ZE/EkBpD3B65zG5lK7ePV32UlrSTsGNbctWg5ufLZ8XhINjn2VjM2YjCYSnhYb/YT3ZdyzJsYcz3+XdALf5vjbiXOmmf65NkhFdDl+m5CGcmqe/ERyfS1vL7PtG9EmmOHRjIpy0ty47g+3pBc4P2eQmU6Fhh2yT/bnMee3nu+euN4KqbQCwPR0MerRKw7JtjE2SadEOCFpdA92dNr2gjuVKpNdheyHSu6+hTgAy1dtWxrm0SRU3W/q0rYv5jI4Mzl326erzYe19Mrru9l/Z79ICgqz4lqt+Jfv/9cxcW37fOnFaxVL2x5J82YVk1OUYrVyu2FV1leXSjHe6jGvTVu/3i8i28QgHWC75qnqSib9ZHHbgWu/PZS5wP2+i1vzW7cMAgFY6WdITaWAgV4s0SaZFzQbaHfSxvQMdjU7N2dlpjbu3chG1GY2ZmWgk0Bu6EqOyTJPLHFXcxQ310rryTJNzCjpCnRhoqk+fZTSQQG7b1X+6Ke2RSfcDO6SmyO93uh+70svU8kn+ptDTU+vvs/t7eJtqWxRfSpN97klUUqWzLN1Mu6y+GRyfSz6zP5vYZ2GFl2uyLBHS4z55lePXR9S/vrWMcffNzMB2pX+yvU4rWu7Pdpyebwr7wHnu54nwcrSf31Jata3xbwEAVLkcHRlsSi+8/uojF6Lkb98+IyhXoqRJruQ6aS/Xu1Q+jVaKahaSl9PG0ss0OY+op7QjA5/W93ikvSRpRw/Jc4TcQJRK2ylot7WZ5zIFU+F5HstnuGgmVKt86b4if+W1zUtpO4ZCScAm32c76pDhWkXU75f2vQQWdjotgTj3vnSTHhVXlst2Ve0yylq9KVgiEptbSfGB2drzji4DPb71r+xfdQpd6mqzP+v5RkvcdR/Inm/Oez8vWw612XGTqdZHYA4AW3WJgh65QCVX7jRuqU17gvIxiPa4dHxsewfKXjg3U6PL6q3qmaF+zxa6aGveDTja06o7vq2C7H+a6XL7oT6Ys3G3vJWW3QprZyC+BDLZ5yPTt514bK8aUW2uExGVW64kN1m9XHIcJ5npUYv2fXtuhw8n9b2obT8T3nx/TtsCbU27/bx8OTY9bjZ5cCsAoMqVO3fu3HevG7l79665du2ae3fR9E6hVpGQ4EEDhpV8uJakaN1xuXhr6YbePZQri7u2yEVKLi6x9pCk9a0lIJhnqr1JkHNkq8zoBXpo3w+yuQXJ9Nvqc5nM2Dp6MVxEQ9uzkFa5G8wK3yni8bE5ke/RB90tb3bKMvuFTi3Shxvmp02E59OOn1dvOM93pKDrUtZBzZ+PrVjuC3b/3Rn3PVrtUfZ17UdrW18XT05sl83RaG7mLUqMtHfGpXXL5c8R4eOkyM/br9vls3/kPFCjBPTcpOen9tatj+QcFZnRfL7Dh2Q22J9lwu2fb1rs55XLsbvjBgBQ7vbt2+bq1av29b179+xfryNBj1xiXIa82jLDUjp9b2jmp8WHHBaDEq1iM0k6Nlj3lSV8RkOrQkwWWtIzygdNepdQ5t2TYGp5sYzN+PgkfIc2s9xZbYOemXwu/1BWbeuTvNpGAAVsGvQ0kWTc5YVWfVtbQrUMkFb4myb7wgc9slzzhiW3C1n/yUerjme3LmqtNwAALtalCHosFyiESWCRjSoks3Bs67pkRbadTjpZPDbHkivoT9d0eKB3/NzLaj5w6JmefEmbDEQSJLk3ejdR5hJJYDQchXunaxv0hINCVw2vcmUA9Zxn0JN8l6lZWqGN1G+snEu0nd5w2LIXyF3RGzN6nOpNj4bBmG2PZz+qz7NxA4viiTlJVtzOtxEAAJu6PEEPgAPiqgBtvaoStkY20GyxKJQ4AwCwnwh6AAAAAHRaVdBDl9UAAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaVfu3Llz371u5O7du+batWvu3cX46Ec/6l4BAAAAuEgf/vCHzZe+9CX37vzdvn3bXL161b6+d++e/esddNDztre9zb0CAAAAcNEIegAAAABgR6qCHtr0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpBD0AAAAAOo2gBwAAAECnEfQAAAAA6DSCHgAAAACdRtADAAAAoNMIegAAAAB0GkEPAAAAgE4j6AEAAADQaQQ9AAAAADqNoAcAAABApxH0AAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIKeumYDc3x8bAbj2A2oMjMDmfZ4MDZ1pgYAAACwO1fu3Llz371u5O7du+batWvu3UXTIGN7AUY0OjXTvnvjSdBzNJiZ3nBuTkeRG+jFJo4jE6WDZXmOBmbWG5r56cgUpwYAAACwXbdv3zZXr161r+/du2f/et0p6VkszEL+RBJ5tE86G51LQ7OJOTk5skERAAAAgP3SnZIeLVnpT83ZShFNA/HYHJ9MTDQ9a1TSMxscGY13+unnKOlB2I/93C+Y/8f/9OPmb/2LnzF3vvhlN7Sdr3/fV5nf+PB7zPB3foN9DQAAcJlVlfR0K+jp9Uxfi2tai81stsgELxk+6On3JYjpmeHUBzOxGR+fmMmib6ZnU5N8jKAHq176Fz9l/uTH/7eNg52id7/jLebPfehXmd/3rb/YDQEAALh8CHpqWw164nhmFguZr5nkqq+l07jSoYUEQv2+/+7YxDKf/LCsbNCEy0BLeD4w/qGtBzyeBj6fGD1GiQ8AALi0Lk/Q07B6WxwXuj5YLMxMBvVHWpqTSKqu9c10aszAVm8bmmgykW9MSnbki23VtmaypUJKgiT5RoKg7vqDf/N189I//yn3bjf+6Ld/rflzT/4q9+5QyLGrNw16fXNzKseWG7o1WkI7MWYkx+0weANiN2L53ht6vgiVGl9y8WxiJhM5aQ7nG6+b2UTO+3ImHQ6X5+yQWKa7MVvY6smbbw+3z+rNq3n2PL4t7nq2cp1oKllO05d9v2L9zAZaU6Fn+jenZriFQ+Twty+AQ3a5gp7+zBxnupVu3jGBXshOTdJsx1VdM0MzHy3MiQ165uY0Skp9igHQ8lzrlqdO9TZXbS7/veiab376X9rSnl3S0p6f+Yu/wb1bI54ZySNsJliyqhktzaiMzHxeJ4hxx0pUd/pV9uaFdkTi3mfl2tv19OZCc9rJSWPpcd0m4xpLZk/OK20WdkUked6hbCf31tLeJt3LjSUdwDQRT07MiZ6jN9jmCbfvyBxG83llht1/Z7DqcmP+ezcNSspsaf5+H6y8Gbj937K/2zfZ75sez6vnl7LjZ92x0O77ATRzuYKeoTHjycz25KaSNjhL8XggQYyc4KdDCTOyQieszHwlmEoCHe3IQMbItaQXD8zJJLm7lO/cwH2uRtDjM2VqdT7oiqP//B+7V7t19t/+ZveqWpox2UQwMxWbycmJsbMuZHpWSlathUwvx4pMO715vXBMOrkMR9byu4KZnVh7VdSu7DVD1zczm4Fqan2Gq4w/tqPR3MwbzSCzDje2uvzZc87GWmVsl7+v+brJ8Jn64n7mM8Ay76mb92UMenIBf9lMagVGTe3p9vXng9r7bPj8svb4ifpmFCo1a/z9ANq4nNXbJIMVyjMs5OQ4iIcS9PSDGazcXRjXXkeL6ZelOz4w8RemUAlN/aBHpx0fj5NMnywTJ8Ju2tegJxrJxTkYaVRYzMxkPDNxaUZJ9n8NZPQAlGnmul+nAUhzZRmn5W+oM17ez26ZyUSOtTiSxRquyUxKMGYfLhwIeuS3DG4sb6yU8ueg0qDNK1Yt8pktyfTKgoc2z+LWDfugZN1+N6+HN6A9181Wl1+rAk3WbIiFbOPkrrScJ6v2Dxm/rupRkM3QavvJm5KZbPxpKxxU+nWX/921MsXCVs1aW8S2kENAt61s134vHKhnRFq9rNFv3ELQUzODnQZGsg6H193AorX7b8C+bt8154yssmmzQc8yv7Ba+hP6jibfD6CdSxj0+ItGc9mTZjw+NknMI0HNIrnrlAY9EhANBhMJVkIZvyZBDy6DfQ161mUSgnyGqvLu8PIY7E+TevbaScgqF1zo3VHJPAczkL1+oXqWSIOosoyh//585ijJsAQCmaKq+afjtqW4PJmgpyTTWyfzVPu3BqSZ4Sb7h78zv3Wh3+C3b3EdhYfX3d/9dNvUPINb9tvWy2bIy6TroO5+7I/zDmxfmTIYNK2oOP7TdRw4/2nQfMPeLFGh76j5/QBaqwp6uvNw0oDecGrmcmLJJnuOkmBkWhiud6NXSEasLxmubOFPSktmTks+B1x6klmYjyRfoMecHkCR6ffleFpJ/k55ZK4Hx0sKHH+zSZKxiEYlJTazmWSMhN5lz3y+Zw/m2MTrimkWy1Ka0jv5ehf97MycbZy6k/nRO9/ZlCoMDyc3rcgPD2yBdPvK/mEHOH64zkxfu3TL5ey1BMsPWya3rUU0nAe2TyHJfp0sqmaIA+MLKR/wSKZdMr0nlUkz9fWnDZXaRcVjSFN2MUR8a5YcQ3qdC05f+IDIb5fM+MLwcHLTivzw89u+8kFbMqnnAK0GX2bt+aWElurN0/0jdJ6p9/0AdqPTQc+mor4GNpJxc+9DFisn2OxJdhEYJ2l5Bga6Kxq6gEdoVa9gSkarxco4n9wEKT2O9K9WrcrkpFJx0nuUZDC09CgrcnW1FhLUVIldByhR/7rLwGAtrcpYuJmU3BOS7XAzPzyYbrrMogSTN3Pjiu0j/PaVSXMZ5uXwpCR+kKaxO+lqm87s8CRpT2xFMi8Z3upUPRvYTm9Kuf16U8mxEYretfOKqZlO8ylfxU6rqOoyyDEk67c4rU3Feq9d2b4+kNKqjMHNsO78soYss1/q4Hlm7fcD2JVOBz2LiVx8CnfG7LVoMVm9g9am2D6emfHKCTZzktW2D4Hx/gQNXA5Vd6zdXW3NxATHa/J3vh29caB/i3eBnXhyw5TWUOpFNuOl7XvKj8LY3HLHaE+mb0WrxxwdlVYH0q54B5KBq877atsRd6OkkPydbRPfCo7X1Ljjyh2oXbIm0pKHdYGmZBaD21fXhZ3BaKUkf+oaXWrJY3Z4klarkM0Gsg/NxrLvNQx8dLvLNtcuy0+qcrQrGf9s8sujpaWh8ZpubtTTZ2x7HdXFaFaSUXSY21fOG3ZAbGbpgZShx4/+LTm/bG7N9wPYmW4EPXrHS/700ha3Pdtr2zBwAiyv3iYnf/lMZaPdosDJN0nuJBv6Hkn+BA10iT7vIxespHfK9WG8yzvIo5EcN5n35WmUVMmRjFX2sNQMvdLjdZW/g10iup7MU84Z5fm0hQtGfOakimRc5HcfFQMzf7dXIo/VpZHv1oyV9iRZmVmMS26qZO5sS8Y8NF5T1Wo4L9H1pJMDv82qaEmfqg40M3f7C9IqSZKp1qpZ2SpUy/0nPzxJblSGb4emwXhp4CMfzF0uMm1kIi0VaVNKcB4WE3PD7hwtSzIyDnb7ugM7VFJWfX6pQY9t97Lst1Z9P4Dd6WBJj2RCZgs5MerJeCF5jnxKzrvytzBcPmFPRJHWCXYn5zpWT7D5k6zeCVsZ78YB6huuvd389e96f62k0+6vpNpOmtLruezzcmzp8dWX7MB4PE4yNtpBQWa4ZtS1gXI6LHhHWAIGO1+ZZy7HmZjZeVSRDJL9nJaG2AEr/F1wWQBZmnV0CeV8oc89ys2vl5wHZPjKzVx/x9r+VjukRPmdfn/jRHtvC43XtNLc0C5jvZSsY43ZwuNXUtkp0weZOk0ypITOR/+uWSelpQDa65q+2Dwj72ngY1dzVeDj2JIdH/BoxwUrK3+PaAc786nsW1PTT9dbS4e6fV2J7+pyV59f1pJjTB9ibkUjU6hdu1T6/QB2qRNBTywnDj1PaUChJ53k7ujYZq6Kyd5Z1Qt5YJwme5e0+vYrgADtOSlpvO0yiyH9oWTYJSegmYOTI1saVJphTktcsvywnuYb8iTjafMbktnQryijAZXSB4CufvWyapufbp10fkmuzvHBVTb4c3wnCfK5td+QvVmSScv8mAZX4WmK4luT5PxWI/mMZ7iNxGqalFbT0Q4qdFlmEuiWTSPfU6u6lS8FiMyosIHTqlMbVtfKi2x7F53fuipZyTjtCl0Cnn0t4cnSzgsiPQblujc4qWxbEuxoIHWg2zdtd7PQwzGj4vxSpNXk02NAS7eP0hJfbQ9c+Sye0u8HsEudCHq0pEZyELk7SNpz2+np6UpKq7fZ91PJHEkGoe+nbfHMko25Kiz63BM3BJfLv7n98+YPfO+nayWddpu03Vs281orScakzb6q7Vg0ntBG1nqnWY89f8MiTKuoTs10mMlwSgRhp5dMffFQTYIObVQdfrZNSgKv5A5+oBTG32muuktbVHLXNhwMLd9XZyZ3IFuytib5mKm0Z69iqvgp0TDJqMZjV4K2wldJlKDhemk2UTLOrq2W9shX+D7tdW0+lW229YAj6aFtbSCj1dkk2G/7TJrtiu2xVjxuV58/JL8tqcNnxsXqmQ0c5vZ1JbFFFeeXFRId2VJOm7R0W4bJ8TKdy/4yXffw0ZLvB7BTHQh69IQjf3r+zkl99iSlpT6+OssF0PYAEz1pykVqo2oGQAtxgypPy1TMPNUQyzEmgb12VpDcDU3a3Pnumn1vaUVJtbh6uQPNfEejmzW6f/Z3p4vdxi6rpvQlI1c7T+Kr+BTv2vpgSH7bcrCuP/1blQEMlXBtTrvTDbedWk0+06k3j0Lji6n64ZuyrW2Uman6k7GsTpjvXrwo6XnPZ9RX6e8Lj6lJ9tFioBBMPuiXYyfUUU0ohR54Wqu3wpVxPpXfKlB6M2HluA3tUxKsJatTj82GnTakDmT7Bsm6rF6V5bRNr9v/7c1THaYl2I1uCm3w/QAaO/ygR0/m8kc7Llg9n0qm5vjYHNt0ZI6O3EPFFknPStrYOjlHazuDdqf7TS07XwDOX7i3ozVp2uKBu9p9td4JH8lxmlZtW80gbaRB43F/dzqtEmdfurvdaUawrmUQleuNyVdhkUxqmq9J7yT3TcVN74SMP/Te27xo6Hoby6xvS9vCJLf3SzO7KTnHawYzNNVKJxqFlDTcr5hOF0oi1tB6XE3pyg+MK0nFbVGrt0I9TkLjNS2rIK6KzGief1aQfV5QSZ3TbNulG5kqamU3IkIOYvtum1y7fUln0mmSOy/Kb86uRwD748CDHglqxnoy65tR8ISuVdckRfo3c5czm4GzVRI0a9Ish1B9hzyWJVMlz+nRJBkhFY1OzZldjhZPxgdaiq4nx8NQSyP0+JDDR/M42TYhy1QY17/uqp01DdgjY6u22WNOMguZ+mNJz04y73O7B7DMhNlgJ82sSYZxXb023yZHMj3+rOOf/5PvCttXYdFj3g5Ytk1Y00bEknPMoffethSZoXtOiy3d1mWLk+6dVb8ks5unGUz3chck2C0GCqGUPU9rpwWhaYppNRiXY8FlmFeTHAdumuBDRl1au//Uttw28fhGy2fHHMD2DdriOScampsuH1J/PW7x+wGsdeXOnTv33etG7t69a65du+beXYx4fGxOtNMBvVj5K1E8NscnE2OGc3NacmdrKTaz8cTEUXIB0QzERG/JZefnSaZIn7nRc/OdDVypUVuh70BnHf3n/9i92q2z//Y3u1fNpPtzVGyAG5vJyUnazqWycW7Kfca46fVu75rOQbTKjqVBVfIqR4+75HCZmcFRUj1uelaekUp+j97xTqrPlSkex9oZw7rDMp6c2LvXtpeudObh5fLzT6Y1bl2uWS7NLGpPYLq+b153A/MWt26YgV2Gqbl5PZxrWshy1lkHIX6566yPRtx51Gb2ZD9JdqvseqzJzafuZ/022/j3ZLbNqDeW4LPp+tXSm4lZ9PrmZmm7jzr7uBxjgxtmtuiZ/s3lwz2r9vt16yCeDMziunyf+1x4P1/jYLavP68V19X6dZ+eM4LX8Lrny7LvB7Cp27dvm6tXr9rX9+7ds3+9gy7p0btgPbl4rC02LyVnmswDRG3AI/Mb1mjB3KpaUDZtdOUFtmeZ8ZcL8M3iRXp5B1dvKKw8KHQrXMAj1l/7fenJdno9yt0xl0xKnc4Lws8bWS3VUT3bEYDMV+uypQ9XXFO1LVuSpEFgIC3DnPJp9pJkFJNTn/uNss5vHkyOTwMW7ZY6OU6GQz0uYjO+0aQtjFyv7Pm/zs2DKnJc2mvQMuDZVDRcBjxZ1c/VKTiY7VvWS9um5xfZLv4kIufL8na6DXqJA7A1h129TU6o2uNaLp8iw/SiUvcBoHpnKFcVQXt0C31UTuYarCznG85o1E5uLsBF0qooScCju3jJHccoea5H0mxF74RWd3G7Qo6dmzdvLgP+QvK15Oxd3cB4Tct7BBVdQTdkn60iPz79KTaoW5eBLXuOh2vXY4/t5Rw0IznVBx/LqLpV23xbinPv3W3XZN+ZZPY3ywfSTfani2DboSUBf9pZhq/OVGu/OTw+uK/tkLavVjHXv3K85o+yLZxf0sBPz68lN4lKvx/ALnWg97ZVuwoqCFbQ1rvf8Vb3ane+/n1f5V7VEbuAJ8mNrK0WYrti9c/f0S5uC5mbUlqN46ikepssg62C5d6NXc9uazJI+sBfpW3j2kl+uw947NPzfSPkdZm0itIa7VZX2+eFezLzz/+p7ra3/nQHRDLPur512yZtkSIz0lIK3yGGDSi0Yxld72Ur/uIkDx5N9olidSvd5va40f1GbwbY33fJHOD2TW8sBG5AbH5+0fOpvxkrvz1woqz6fgC708mgB9g3v+/X/WL3and+47//HvdqnaQ+eRLwSOa6dj34yAwzgU9SLa4iEyOZnRuSEdT67SsXdpsRkmVIKr9LJinp9jXNIFU8tHTZ85q7W9qEdkuc/naZl2Zi5cdHxdIs17tccREadUSQVbdqm5+uA9VetLOXpIvy5b5mA0ztplxWtHYuk322TTq97DO229/iyj9nyfL4/UCOk5L2Jcnxo8Nj+6DPiy/Z0JsSSW+l2aRtXuorK9FcOtztW/3coI3OLynfjbeQoDkf91R/P4DdIegBzsEf/bav3Wlpj857+J9+g3tXwWU8kvyPBjzZqmN1ZAMfvZ5LJq9Y1y3NuGu9/uQ7fBu2eKZBh2TCfMbQVUcd9pNuX89c4GF7JdPMVPCZF76XJ8k81K1nJ79bq94cnYzdsrm70dlMrC/N8pk0vcPvM2h2wMR1jesyK9rYupCxLE22LYjOQ0sEVsf7TNFskky3+ZPn63ZZLdtIM3eaJBdqnwEj26i8LcIabj0njwRY3tlPStNcgGmHeMn+oSVkywd7SkA91tIVXTdaIlceAO+C30fT4MXtF9Ng3c+E38d1Ch8sXWjmXmslFJMblSPLmvQo6ra9S7OBe1BoMfjuwPZNg5nS5wZVn1/SKvFrTpy25NdVm89Nuvb7AewKQQ9wDrTq2Z998lfsJPDRef65J3/l2upt2Wo6mhnRXoPWXLdL5AMfrZaWu5PpG+L79nW9RRJwaEZp4IIOyYQlmaGkvUvKZjD1mSKaeXKZI9ubUl7fNiLX73YPPqygv3tZ9Ua/YpTejV7lGoinmbdkGbTakg9IspmVlYxly2Sld4RdUNXESgA2CGdaA275p/dLkJk808T9zjTzV5Pt2Uy+V9aVW9OmP9I7//qEelmfVcshI+3+oCUDdtt7Oq9efh/ZkSTIzeyjuvx2H3UlgGv4Uo7l8i8z9/k7/bsmx/ZN2Yc1CMkk351ynh6buv3dtndpWRKaCb4PfPsmYglkdGPIOqrotaTJ+aWZet8PYDcOustq4ND82M/9gpn8v/6N+Sf/y2ft601osPP7vvUXmT/67V9Xsz2PZMIGN8xg0TPTm/UyctW0mtwNM+vfXKn2E0vGSJ+R5d6l3bhGEtQMb2qgU+PL9a6yPs09Wp2/qupSt9h1r32/kICq0e/WQGdiJnHfZupspu9GLAGfzMNNsV3Jepr1V39PkXYvfGO2MD1Z97YEQksXbkwkC5uV7864jM7L39BeaPFQTwMl2U7X12RkQzRw0CcG1N3GVez2X5jroZ7ONMiTSGLbXRr7/aY/vZlU0XLD27AlZpLBXQSOj2qyLdd2WR2mJa+TQjfWXlKCJ8fLMDtO9rlBthMGfXiubH/55f3h9dVteODbt0k33E2mrWsX8wSQV9VlNUEPgHOg2aptX+SXwVT9dkn7Tu98b5bZRgdoNTPdD9gRtseWVI2T5181ed7Yts4vjb8fQBsEPQA6yz7YVHKHZCIAlJNA0p4qmp0ptnd+aff9AJoh6AEAAADQaVVBDx0ZAAAAAOg0gh4AAAAAnUbQAwAAAKDTCHoAAAAAdBpBDwAAAIBOI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg067cuXPnvnvdyN27d821a9fcOwAAgOa+/OUvm5/8yZ80Z2dn5t69e24ogH330EMPmV/0i36Reetb3+qGXLzbt2+bq1ev2tfF8wlBDwAAuBAa8Pzoj/6ozU9oeuABKqAAh+DNN980n/nMZ8zP/uzPmm/8xm/cm8CnKujh7AIAAC7Ez/zMz9g7xV/zNV9DwAMcED1e9UbF+973PltSewg4wwAAgAvxxhtvmHe/+93uHYBDo4GPVk09BAQ9AADgwjz44IPuFYBDo8fvobTFI+gBAAAA0GkEPQAAAAA6jaAHAAAAQKcR9AAAAADoNIIeAAAAAJ1G0AMAAACg0wh6AAAAAHQaQQ8AAACATiPoAQAAANBpV+7cuXPfvW7k7t275tq1a+4dgDp+7ud+zvyjf/SPzA/+4A+aL37xi25oO+973/vMN3/zN5vf8Tt+h30NAIdmsViYRx991L0DcIg+9alPmV6v595drNu3b5urV6/a1/fu3bN/PYIe4Jz8y3/5L80rr7xi7t+/by/y733ve92Ydj7zmc/YE82VK1fMBz/4QfOt3/qtbgwAHAaCns3F3/2oefT52HzwY/fMxz7oBrYRf7d59EOvmSeefdZ85IORGwisR9ADIKUlPC+88IJ5z3veY77ru75r44DH08Dne7/3e81nP/tZ88wzz1DiA+CgEPRsgQYrjz5vJOwxH7v3Mfm3nVc+9KD50CsmDZ7iV77bPP/ya27seo88+zHzkRqxUhzHxkSRWU4ayzD3MkemqRV7JZ+P6k2MHSDoAZB66aWXzOnpqflTf+pPbS3g8TTw+TN/5s/Ykp4nnnjCDd3UzAyOBmYWjcx8PsxcnFqKJ2YwiU00nJphdmZlwwFcCgQ95eLv/pD50MvBaGCFDSRULpgoFz3xMfOxbITyyofMg0nEY+654iJfglTX+pKm2Hy3bGudZXZaH2yVkt/0wSeeNc9+5IPh3+aDvuhZyXx/ZPPrFRo7lKCHjgyAc/Cv//W/thf2bQc8Suep89Z2QuvEkxNzdHQUTCcSfHjxZCJhj1xr+te3cwFZxGY2m5l44d57ZcMb0It9/rKsd/2Kw8rZaX2GIRUaBgD7SUs5bHLvm5Fg5HmNOiLz7LPLqCX6yMdsZvZTLjqJnnXvc+ljJqkJF5lH1ny5BnEa8ETPfqpZNTw5F7/y/IfMow8+aj703YHzcvQR87Fn5cvj58PjAYeSHuAc/OE//IfNd3zHd5jf9tt+mxuyXX/v7/098/3f//3mr/yVv+KGhGnQczKOJZjpm/SezGJhZnJRiUZzM9filnhiTk7GchmUC2i/t5wuJzL94dD0Mxe5eDIwN2Yl0YsGFvZFobpC2fCi3tDMp333xovN5OTEyM8x/emZ8aNngyMz0IitPzVnK58pyPzW0Xy+LG3yw7dV0gUgiJKePeBKeTQY+ZQt/ZEgyL7/SBLQrIzPCJQQBVVUwUtLeoLzkGvEKxLMSFDm70NFMp0PxJZ8KZIEbhKM1almh+3RAJiSHgB7SAOWqZlOkzS0cYEMu55c7CY3NAhQcrGZzWxJTCithDe9yJ70gslFNFFUc3gxyfiieHLDBjwarK2LbRqLhubmSJYtHpsbmRIwALgoWlKiAWKrZKOKkFfMh3Rc9Gxa3c2WyLhAo1q4hCjklec14NGvebZhm6MoCXI+dc98SktzRCyB1qMrJTqR+Yhdhtg8v3a5cVkR9ACX2sxMbOTQNxrzzAZJyYkGEmdnZytproGA6g9X2uBEMswHUitpmAQtvUywtTpcIpeFhFIScOi4frSQwKrvpit8WTwxN3RBZfxwR42BouFNk8Q9NwxxD4CLl1S79SUe9bjPuHdFr3zoQxL2SNDyMd8W5hUJGnTqD5qPrauDJoFRMumza0pWXjEv2zjkg+bZDYpgoo98ahn4PP8hsxL3fPCJJKCS5aKWG0IIeoDLTEtt5I9WVTOTk7RamK3mVjQb2KpxEhEFqpolkjZDAzvPHJ3nPFAiUxwuF2dfgtS/LgNnk2DAMZskpVHRaChhz65EElDp3GMznqz8IgC4EB/8mG9PUyN97FkXzKzSEhNbACQTvCZBxIc+9Kh59EENgvQ7VnuBiyVyWZ6O65fymFdetvNMg5INRB/xJUWhEp0PmifsyFiCLKIerCLoAS4xraYm4YX850tOIhMttD3LyUo6shGRiGfmRjo8EOB42jNb5vM3btxIX2fTcngyLy1Zse9vzOTSFZvZDQnGcpGPVq/Tv7sr5Un1dd2IkuALAM6fL/Gpk9xHAqLoERsQadJpZbb6v0Y84Y4GYgle0vnF5jX7WoKPRx80Dz7o0qPfncwj45WkmEdink1DHuUDG6G/z730/HfEycIBOQQ9wGUlQYkWYNjSEq2uJhn86Vxeh9rUSOprAGBT8l4vdlUXVLWwE6zOK5z69uKr//hh+n36N9ekx5VOycgdlvJ4+nv1rwRft7iIArh4r2iJTKjdTih9KGlLExT9/9u7m92mtS6M4xsBQiBRIaiExAWAYpQBM5gyZda0ihRuJWmUNpfBtJUQSWfcAtMO0HGExA3wUeikgwqhnrX2R+LYjpu0aUqc/++V3yaOj+Me6cR9svZee9t8/vvXV4XaNkRoqEgPa7OByD1KVFAi83xz0y6M7bbIfX5nhPvE+d3dphWFE8X/ZX83H+SG1SUggdADrKj4QCspGjDcDaRm59HUTDPMtzln89NxzpWZxzNx893gKtm5Qc1EmzhXndLMc/WRR4X3iS/TVxsA5mRew9uS3NwePXfx4qZxp+PDRGS25dj3YduKXADJtM0OFaFzOnTOYLQIac4Xb8P3P/9LOaweQg+woiJtOS0/+43xIWrjQyMmbzPRFtA5awMltzB6biS219btJ98rtr0O7A10itCVd91j2zQ5RkKhvYmGChMAXAOdyK8teN9rVUX/uJ9m23TVnMJ20n5uz6T1c9xQsTB3JzQlGOeGsOXM79HPWf0ZKjBXbn4VJZQPoQdYWdoZzcYe0w0TVrQrWmK+TdGWDSlB383lGba+DiJTa4XqTctWdaJaa1TNsS2v/aHWwFZ1xpf+Geg9VFQ0ixTTxgupa85sjfQ15tB22vbBQNdSBYDF0fVtwnyZOW1j3av1/DbxbBrNK3GvZ3q9HVOv181OuhmAb0TQ66Tm7cg5XE8DeX0BgUO/sHI02PmHGaHCBIwQeoBVVmu6tsz9A/9tXNPsHR7ajmrnbbkN3Cp+3o+dk5O9G0Ub/vXahvvWL9rwz2ujm9dAgo3+9JWVsfPE2sZayMHTjK7L/eZzbPMHFqpMeRwAXBFdRyc5nOwCWzuTSHQRUj/nJ+6Zup0DpF3cOhJ8euY/+ymtVXE9QD8vJRhpy+i4Y+qJntBhDZ7Ndmh7fbWGTQoWVj1CWRB6gJXmqytx34R5+vnhILvlGVurp+kbE1yQnbsTtfziqRfg22EXbnutGa5xyuFwADBv8pk7ahpwwe25P9eQnHPLvdZuazBy84B0CJ0bRieHSBj6oPeG0OnNt4wezu0Ztr1u20rRlQtVJRHJtU/+/GaYG7IIPcCKq9gA4/6g18VJc4eB5Wzp4W2xBCc7HC1smW5nsdl9EebwuEVQ411d10ee+5O5a1F6DrlthYoQAMCZYcjb2FC2HNG2qwJtb2v4yflCy3dIGwUMX+2RyFOv1t3QON07qcoj57P78zqtzSxRmdLruMRCp1hNhB4AczHoNkyjkdh2XXe4keScntGmw+vGxLE56HYl9ixgHZ6ZyM17mjF1ALAA0WbBkDcbTC5IPoN7O24h0tCgYCsxNE6Dkj197BcrnbSujxUqLmGY3EVJ4KlWTcefY3KXufl3i0N5EHqAFRQfSKiwFZmwKKnOq9ERYW7Y197enpvbs9e0c2dqe4khYcNNQ4t7XdX2T82pnegTmdahPD7MDh0bzekZbcnmBZF90je7ck12/SC3eyQ0FZC7JyPNAKy054mha+ltlr/4NeTY5gVVVyGScFHvfJDdkxoUjAeK4ncKx16gsYBcVyzBakfXJboZAk8kgccPvcsT1ufJzPfRKlHd1H2YG5l1P5YZoQdYQbqigqvIuO5lGjCMBKC+fhXnO7i91Y5ueteQfbsvxtta6w3h4G3DdCV5FN/wUgZ6ExvfxoT20BOrPKGpwCI7qc3QMQ4AFiT+0LFd1nK3UBIpEO/URyHHNi+I5fPVze/5/Pm9Mb5BQXrujC6O6ka1uc/ruCfnqaY6uiWExURd1aiAnicxNE+vq1qtS/CSe4W+Ltf2/nN+W+0g3FPyrrmjnek6ej6/U8y6H8uN0AOsog2d5L9v9rVic3pqDpsD05cA1NAUM6Ymx2jFRttQd+2Nx91UXAMEnZMzuXV1moSnnDlDyX8+LJg6qvLEpqvHhJba/n11/8KaCmhFTH9K2koUpQDgevkvjnI3eXlSw5kg2tRqiPxPh8n5BgYadnR+TxRLcLJ/7CfnzugQM50n5O4BbbsAql/8NO7YgJJo6jYUmh8MqzCzkN9heH1ybcUtsXuS09y1JYfjqRC80mbdj+VG6AEW4N69e+bXr1/+2fzpuR8+fOifTSeKaqamNxR94v+w1+FmGdrGutUy+3tNe+ygq80HGsbs79tgklzcNHYrhzpa1ZF/YjQPxg97k5B1enpo5/JELT9MrlkxsZz3hR1qp/fyZKKJTT/xPDQ7sN3dFiD8TjRVAPAviWxFxnVcm7QlqyLDVs9BpAuXynHvt1PD4XqmHhoUhLkz8Y5taR2GmGngsVlIzxHCSCyhQ0KRNjcYf6dNCSH6U17PSUU6XC10jMts+ntkrm+CEKokJKV7HNiFXfVccs7kv5NZ92O5EXqABXj58qW9AV1F8NFz6rmfPXvm98wqNt2ujTwSetyetKjZtIuJ6rE2A8gNqKJVoNTipoNYfkqY2pBjxwJQARu+zIGdWxTVJABpGgrVFT+0LEp0ELDXog+Gx1wl+d1sEItMTX8pALhuNqz8NZ8Lu5dJcEkOFZMtVG5cAJlEqzl1Hx60QUFsh3rdrHaMK/C0bVAae2s/7CysA6TD3dJDwjbbriI0bHU9d3LdbgKSaU/qnR2+5EubdT+WFqEHWIDXr1+bGzdumHfv3s01+Oi59JzqzZs39ues4u5b2z463TjALliaHC6hW7/rjg1Vj7AWjs6/6TfsULXwmg1A086D0WqSNlGQEFXb0PV9tMmCf02ML3QawtkobCXZhgpaTcpdPTVF3leH92nlKXcKUQhWtWb+6wDwT9KuafJH+9i2adpalfFH5IvMtlZ3NMjYEkckgWVL/l9/avVj0gKk+s9pVWbTHpepjkhQcx3l8qs9l6Xzk7QKpdWvwiyIlXbj+Pj4zD+eycnJiVlfX/fPAJzn06dPdkjW2dmZqVarMw9HSwsVHrW1tWVevXplHxdxQ8iMaUlQSf4RH2vCkCQRdvUbdwrm6ugwtWxIcOeumP3TfVOTAGTX3pFQZMNH3DUv5I2LbnV2qJs/qXt/eZ9WxezuDrLvNzxfzb2f3z1fbj7Rbpz/+wK4vMFgYD8PsQq0iuSGyBV2YJuVrltU7ZhYq1ATQxmukv4tUkm2Yr1GP3/+NGtra/axDpFMIvQAC3R0dGQ+fvxovnz5cumKz927d23Q0SrSo0eP/N5ik0JPHg1C2QFqFVOp6Xd+ebQapN8m6kMNJX1TG75PLIHvnOFuFZ1j5B8bbZzQMH1NSVHLHB66+URJYQ5QMizN01WfHwChZxXpqAG9UczvU1XvPXpKPqevC6EHwD9ocTcHvbFd9n2KzxEqMW5I2zSj2aYWKkkTAheA+SD0AMuP0AMACzD/bw0V3xwCi0DoAZbfsoQeGhkAWGoaTOYfTXSYHoEHAICyIPQAAAAAKDVCDwAAAIBSI/QAAAAAKDVCDwAAAIBSI/QAAAAAKDVCDwAAuBY3b97MtJUFsDyW6b9fQg8AALgWd+7csetqAFhOv3//Nvfv3/fP/m2EHgAAcC2ePHlijo6ObPCh4gMsD/3v9du3b+b79+/m8ePHfu+/7cbx8fGZfzyTk5MTs76+7p8BAADM7s+fP+br16/mx48f5uzsQn+SAFiwW7dumQcPHpinT5+a27dv+73XT79AWVtbs4/TX6QQegAAAAAsvaLQw/A2AAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQaoQeAAAAAKVG6AEAAABQYsb8DxNzEJ3lS0Q3AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 额外的编码方式 (10%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过补充`ECCompressedPostings`的`encode` 和 `decode`方法来实现一种额外的索引压缩方式。在我们课上学的就是**gamma-encoding** 。另外如果大家感兴趣的话也可以了解**Delta Encoding** ，[ALGORITHM SIMPLE-9](https://github.com/manning/CompressionAlgorithms#simple-9) 等。\n",
    "\n",
    "你应该以多字节（而不是bits）来存储倒排记录表，因为索引的长度和位置都存的是字节信息。 (4%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ECCompressedPostings:\n",
    "    #If you need any extra helper methods you can add them here \n",
    "    ### Begin your code\n",
    "    @staticmethod\n",
    "    def _gamma_encode_number(num):\n",
    "        if num == 1:\n",
    "            return '0' \n",
    "        binary = bin(num)[3:]  # 去掉'0b1'前缀\n",
    "        length = len(binary)\n",
    "        return '1' * (length) + '0' + binary\n",
    "    @staticmethod\n",
    "    def _gamma_decode_bitstream(bitstream):\n",
    "        if not bitstream:\n",
    "            raise ValueError('空比特流')    \n",
    "        if bitstream[0] == '0':\n",
    "            return 1, 1  \n",
    "        length = 0\n",
    "        while length < len(bitstream) and bitstream[length] == '1':\n",
    "            length += 1\n",
    "        if length >= len(bitstream) or bitstream[length] != '0':\n",
    "            raise ValueError(\"无效的伽马编码\")\n",
    "        binary = '1' + bitstream[length+1 : length * 2 + 1]\n",
    "        return int(binary,2),length * 2 + 1\n",
    "    ### End your code\n",
    "    \n",
    "    @staticmethod\n",
    "    def encode(postings_list):\n",
    "        \"\"\"Encodes `postings_list` \n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        postings_list: List[int]\n",
    "            The postings list to be encoded\n",
    "        \n",
    "        Returns\n",
    "        -------\n",
    "        bytes: \n",
    "            Bytes reprsentation of the compressed postings list \n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        if not postings_list:\n",
    "            return array.array('B').tobytes()\n",
    "        gaps = [postings_list[0] + 1]  \n",
    "        for i in range(1, len(postings_list)):\n",
    "            gaps.append((postings_list[i] - postings_list[i - 1]) + 1) \n",
    "        bitstream = ''\n",
    "        for num in gaps:\n",
    "            bitstream += ECCompressedPostings._gamma_encode_number(num)\n",
    "        padding = (8 - len(bitstream) % 8) % 8\n",
    "        bitstream += '0' * padding\n",
    "        byte_array = array.array('B')\n",
    "        for i in range(0, len(bitstream),8):\n",
    "            byte = bitstream[i : i + 8]\n",
    "            byte_array.append(int(byte,2))\n",
    "        return byte_array.tobytes()\n",
    "        ### End your code\n",
    "\n",
    "        \n",
    "    @staticmethod\n",
    "    def decode(encoded_postings_list):\n",
    "        \"\"\"Decodes a byte representation of compressed postings list\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        encoded_postings_list: bytes\n",
    "            Bytes representation as produced by `CompressedPostings.encode` \n",
    "            \n",
    "        Returns\n",
    "        -------\n",
    "        List[int]\n",
    "            Decoded postings list (each posting is a docId)\n",
    "        \"\"\"\n",
    "        ### Begin your code\n",
    "        if not encoded_postings_list:\n",
    "            return []\n",
    "        byte_data = array.array('B')\n",
    "        byte_data.frombytes(encoded_postings_list)\n",
    "        bitstream = ''.join(f'{byte:08b}' for byte in byte_data)\n",
    "        gaps = []\n",
    "        idx = 0\n",
    "        while idx < len(bitstream):\n",
    "            try:\n",
    "                num, new_idx = ECCompressedPostings._gamma_decode_bitstream(bitstream[idx:])\n",
    "                gaps.append(num)\n",
    "                idx += new_idx\n",
    "                if idx >= len(bitstream) or all(b == '0' for b in bitstream[idx:]):\n",
    "                    break\n",
    "            except ValueError as e:\n",
    "                print(f\"解码错误: {e}\")\n",
    "                break  \n",
    "        if not gaps:\n",
    "            return []       \n",
    "        postings = [gaps[0] - 1]\n",
    "        for gap in gaps[1:]:\n",
    "            postings.append(postings[-1] + (gap - 1))\n",
    "        return postings\n",
    "        ### End your code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过+1偏移策略处理0值问题：在编码阶段先将原始docID差值序列的每个gap值加1（确保所有gap≥1，避免编码0值）,解码时再对gap值减1还原原始差值。\n",
    "对文档ID差值进行+1偏移处理,采用Elias-Gamma变长编码,将每个差值拆分为一元码前缀和二进制后缀。通过比特流拼接和字节对齐技术实现紧凑存储。解码时逆向操作,先解析一元码确定数据长度,再读取二进制位还原偏移后的差值,最后通过累加减1重建原始文档ID序列。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同上，写一些测试样例来确保代码正常运行 (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3]\n",
      "2\n",
      "b'I\\x00'\n",
      "[0, 1, 2, 3]\n",
      "[11, 63, 75, 92]\n",
      "5\n",
      "b'\\xe9\\xf5z\\xf8\\x80'\n",
      "[11, 63, 75, 92]\n",
      "[68, 70, 732, 1604, 2619, 5290, 6798, 8506, 9479, 9721]\n",
      "22\n",
      "b'\\xfc-\\xff\\x92\\xff\\xf5\\xa7\\xfe\\xfc\\x7f\\xf2p\\xff\\xcf/\\xfe\\xab\\x7f\\xeew\\xf70'\n",
      "[68, 70, 732, 1604, 2619, 5290, 6798, 8506, 9479, 9721]\n"
     ]
    }
   ],
   "source": [
    "### Begin your code\n",
    "import random\n",
    "eccompressed = ECCompressedPostings()\n",
    "gaps = [0, 1, 2, 3]\n",
    "encoded = eccompressed.encode(gaps)\n",
    "decoded = eccompressed.decode(encoded)\n",
    "print(gaps)\n",
    "print(len(encoded))\n",
    "print(encoded)\n",
    "print(decoded)\n",
    "assert decoded == gaps\n",
    "gaps = [11, 63, 75, 92]\n",
    "encoded = eccompressed.encode(gaps)\n",
    "decoded = eccompressed.decode(encoded)\n",
    "print(gaps)\n",
    "print(len(encoded))\n",
    "print(encoded)\n",
    "print(decoded)\n",
    "assert decoded == gaps\n",
    "gaps = sorted(random.sample(range(1, 10000), 10))\n",
    "encoded = eccompressed.encode(gaps)\n",
    "decoded = eccompressed.decode(encoded)\n",
    "print(gaps)\n",
    "print(len(encoded))\n",
    "print(encoded)\n",
    "print(decoded)\n",
    "assert decoded == gaps\n",
    "### End your code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: \n",
    "    os.mkdir('pa1-data_eccompressed')\n",
    "except FileExistsError:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "BSBI_instance_eccompressed = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_eccompressed', postings_encoding = ECCompressedPostings)\n",
    "BSBI_instance_eccompressed.index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1\\\\cs276.stanford.edu_',\n",
       " '1\\\\cs276a.stanford.edu_',\n",
       " '3\\\\infolab.stanford.edu_TR_CS-TN-95-21.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_an-appraisal-of-probabilistic-models-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_bayesian-network-approaches-to-ir-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_boolean-retrieval-2.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_computing-scores-in-a-complete-search-system-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_dictionaries-and-tolerant-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_efficient-scoring-and-ranking-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_inexact-top-k-document-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_probabilistic-information-retrieval-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_putting-it-all-together-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_references-and-further-reading-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_the-search-user-experience-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_vector-space-scoring-and-query-operator-interaction-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_weighted-zone-scoring-1.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_html_htmledition_wildcard-queries-2.html',\n",
       " '4\\\\nlp.stanford.edu_IR-book_newslides.html',\n",
       " '6\\\\www-nlp.stanford.edu_IR-book_essir2011_',\n",
       " '8\\\\www.stanford.edu_class_cs124_']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BSBI_instance_eccompressed = BSBIIndex(data_dir='pa1-data', output_dir = 'pa1-data_eccompressed', postings_encoding = ECCompressedPostings)\n",
    "BSBI_instance_eccompressed.retrieve('boolean retrieval')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results match for query: we are\n",
      "Results match for query: stanford class\n",
      "Results match for query: stanford students\n",
      "Results match for query: very cool\n",
      "Results match for query: the\n",
      "Results match for query: a\n",
      "Results match for query: the the\n",
      "Results match for query: stanford computer science\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 9):\n",
    "    with open('dev_queries/query.' + str(i)) as q:\n",
    "        query = q.read()\n",
    "        my_results = [os.path.normpath(path) for path in BSBI_instance_eccompressed.retrieve(query)]\n",
    "        with open('dev_output/' + str(i) + '.out') as o:\n",
    "            reference_results = [os.path.normpath(x.strip()) for x in o.readlines()]\n",
    "            assert my_results == reference_results, \"Results DO NOT match for query: \"+query.strip()\n",
    "        print(\"Results match for query:\", query.strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请追加压缩前后的文件大小截图 (3%)"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作业提交\n",
    "\n",
    "本次作业用时约3周，截止日期为2024.03.26。请大家在截止日期前将代码、实验报告（可单独撰写，也可整合在jupyter notebook中）一起提交到ir24fall@163.com，邮件和文件命名方式均为`学号_姓名_hw1`，如`1811412_戚晓睿_hw1.ipynb`"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "66px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
